diff --git a/benchmarks/benchmark_test.sh b/benchmarks/benchmark_test.sh
deleted file mode 100644
index 5ab766df564f86a5dad2737dfb96a421bc48067c..0000000000000000000000000000000000000000
--- a/benchmarks/benchmark_test.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-source benchmarks/train_loop.sh
-
-export WANDB_PROJECT_NAME="rl-algo-impls"
-
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-
-ALGOS=(
- # "vpg"
- "dqn"
- # "ppo"
-)
-ENVS=(
- # Basic
- "CartPole-v1"
- "MountainCar-v0"
- # "MountainCarContinuous-v0"
- "Acrobot-v1"
- "LunarLander-v2"
- # # PyBullet
- # "HalfCheetahBulletEnv-v0"
- # "AntBulletEnv-v0"
- # "HopperBulletEnv-v0"
- # "Walker2DBulletEnv-v0"
- # # CarRacing
- # "CarRacing-v0"
- # Atari
- "PongNoFrameskip-v4"
- "BreakoutNoFrameskip-v4"
- "SpaceInvadersNoFrameskip-v4"
- "QbertNoFrameskip-v4"
-)
-train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
diff --git a/benchmarks/colab_atari1.sh b/benchmarks/colab_atari1.sh
deleted file mode 100644
index ac3c646314420c360191f50135510c75e720a810..0000000000000000000000000000000000000000
--- a/benchmarks/colab_atari1.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-source benchmarks/train_loop.sh
-ALGOS="ppo"
-ENVS="PongNoFrameskip-v4 BreakoutNoFrameskip-v4"
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
\ No newline at end of file
diff --git a/benchmarks/colab_atari2.sh b/benchmarks/colab_atari2.sh
deleted file mode 100644
index 5e7b79e72b626a8e93f635a45346670e07b20c34..0000000000000000000000000000000000000000
--- a/benchmarks/colab_atari2.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-source benchmarks/train_loop.sh
-ALGOS="ppo"
-ENVS="SpaceInvadersNoFrameskip-v4 QbertNoFrameskip-v4"
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
\ No newline at end of file
diff --git a/benchmarks/colab_basic.sh b/benchmarks/colab_basic.sh
deleted file mode 100644
index affe598a08c94ea23cecacfab44931170ecfd29f..0000000000000000000000000000000000000000
--- a/benchmarks/colab_basic.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-source benchmarks/train_loop.sh
-ALGOS="ppo"
-ENVS="CartPole-v1 MountainCar-v0 MountainCarContinuous-v0 Acrobot-v1 LunarLander-v2"
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
diff --git a/benchmarks/colab_benchmark.ipynb b/benchmarks/colab_benchmark.ipynb
deleted file mode 100644
index a331841fd9fdd9b85c029c9c573652381d003483..0000000000000000000000000000000000000000
--- a/benchmarks/colab_benchmark.ipynb
+++ /dev/null
@@ -1,195 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": [],
- "machine_shape": "hm",
- "authorship_tag": "ABX9TyOGIH7rqgasim3Sz7b1rpoE",
- "include_colab_link": true
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- },
- "gpuClass": "standard",
- "accelerator": "GPU"
- },
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
- "## Parameters\n",
- "\n",
- "\n",
- "1. Wandb\n",
- "\n"
- ],
- "metadata": {
- "id": "S-tXDWP8WTLc"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from getpass import getpass\n",
- "import os\n",
- "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
- ],
- "metadata": {
- "id": "1ZtdYgxWNGwZ"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Setup\n",
- "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
- ],
- "metadata": {
- "id": "bsG35Io0hmKG"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
- ],
- "metadata": {
- "id": "k5ynTV25hdAf"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "Installing the correct packages:\n",
- "\n",
- "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
- ],
- "metadata": {
- "id": "jKxGok-ElYQ7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "!apt install python-opengl\n",
- "!apt install ffmpeg\n",
- "!apt install xvfb\n",
- "!apt install swig"
- ],
- "metadata": {
- "id": "nn6EETTc2Ewf"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "%cd /content/rl-algo-impls\n",
- "!pip install -r colab_requirements.txt"
- ],
- "metadata": {
- "id": "AfZh9rH3yQii"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Run Once Per Runtime"
- ],
- "metadata": {
- "id": "4o5HOLjc4wq7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "import wandb\n",
- "wandb.login()"
- ],
- "metadata": {
- "id": "PCXa5tdS2qFX"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Restart Session beteween runs"
- ],
- "metadata": {
- "id": "AZBZfSUV43JQ"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "from pyvirtualdisplay import Display\n",
- "\n",
- "virtual_display = Display(visible=0, size=(1400, 900))\n",
- "virtual_display.start()"
- ],
- "metadata": {
- "id": "VzemeQJP2NO9"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "The below 5 bash scripts train agents on environments with 3 seeds each:\n",
- "- colab_basic.sh and colab_pybullet.sh test on a set of basic gym environments and 4 PyBullet environments. Running both together will likely take about 18 hours. This is likely to run into runtime limits for free Colab and Colab Pro, but is fine for Colab Pro+.\n",
- "- colab_carracing.sh only trains 3 seeds on CarRacing-v0, which takes almost 22 hours on Colab Pro+ on high-RAM, standard GPU.\n",
- "- colab_atari1.sh and colab_atari2.sh likely need to be run separately because each takes about 19 hours on high-RAM, standard GPU."
- ],
- "metadata": {
- "id": "nSHfna0hLlO1"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%cd /content/rl-algo-impls\n",
- "os.environ[\"BENCHMARK_MAX_PROCS\"] = str(1) # Can't reliably raise this to 2+, but would make it faster.\n",
- "!./benchmarks/colab_basic.sh\n",
- "!./benchmarks/colab_pybullet.sh\n",
- "# !./benchmarks/colab_carracing.sh\n",
- "# !./benchmarks/colab_atari1.sh\n",
- "# !./benchmarks/colab_atari2.sh"
- ],
- "metadata": {
- "id": "07aHYFH1zfXa"
- },
- "execution_count": null,
- "outputs": []
- }
- ]
-}
\ No newline at end of file
diff --git a/benchmarks/colab_carracing.sh b/benchmarks/colab_carracing.sh
deleted file mode 100644
index 7101140eba9fa2ee8b953ad07d7662fba47416f9..0000000000000000000000000000000000000000
--- a/benchmarks/colab_carracing.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-source benchmarks/train_loop.sh
-ALGOS="ppo"
-ENVS="CarRacing-v0"
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
\ No newline at end of file
diff --git a/benchmarks/colab_pybullet.sh b/benchmarks/colab_pybullet.sh
deleted file mode 100644
index 69e36bac610748c5bd18f5a58a3e79bd39b8b181..0000000000000000000000000000000000000000
--- a/benchmarks/colab_pybullet.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-source benchmarks/train_loop.sh
-ALGOS="ppo"
-ENVS="HalfCheetahBulletEnv-v0 AntBulletEnv-v0 HopperBulletEnv-v0 Walker2DBulletEnv-v0"
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-train_loop $ALGOS "$ENVS" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
\ No newline at end of file
diff --git a/benchmarks/train_loop.sh b/benchmarks/train_loop.sh
deleted file mode 100644
index 9ad750dcf09b706a045ed24cbc2c0a91575a6b4e..0000000000000000000000000000000000000000
--- a/benchmarks/train_loop.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-train_loop () {
- local WANDB_TAGS="benchmark_$(git rev-parse --short HEAD) host_$(hostname)"
- local algo
- local env
- local seed
- local WANDB_PROJECT_NAME="${WANDB_PROJECT_NAME:-rl-algo-impls-benchmarks}"
- local SEEDS="${SEEDS:-1 2 3}"
- for algo in $(echo $1); do
- for env in $(echo $2); do
- for seed in $SEEDS; do
- echo python train.py --algo $algo --env $env --seed $seed --pool-size 1 --wandb-tags $WANDB_TAGS --wandb-project-name $WANDB_PROJECT_NAME
- done
- done
- done
-}
\ No newline at end of file
diff --git a/colab_enjoy.ipynb b/colab_enjoy.ipynb
deleted file mode 100644
index 57daecf4ee497e489387bbb338cd2baa4b01a67a..0000000000000000000000000000000000000000
--- a/colab_enjoy.ipynb
+++ /dev/null
@@ -1,198 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": [],
- "machine_shape": "hm",
- "authorship_tag": "ABX9TyN6S7kyJKrM5x0OOiN+CgTc",
- "include_colab_link": true
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- },
- "gpuClass": "standard",
- "accelerator": "GPU"
- },
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
- "## Parameters\n",
- "\n",
- "\n",
- "1. Wandb\n",
- "\n"
- ],
- "metadata": {
- "id": "S-tXDWP8WTLc"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from getpass import getpass\n",
- "import os\n",
- "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
- ],
- "metadata": {
- "id": "1ZtdYgxWNGwZ"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "2. enjoy.py parameters"
- ],
- "metadata": {
- "id": "ao0nAh3MOdN7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "WANDB_RUN_PATH=\"sgoodfriend/rl-algo-impls-benchmarks/rd0lisee\""
- ],
- "metadata": {
- "id": "jKL_NFhVOjSc"
- },
- "execution_count": 2,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Setup\n",
- "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
- ],
- "metadata": {
- "id": "bsG35Io0hmKG"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
- ],
- "metadata": {
- "id": "k5ynTV25hdAf"
- },
- "execution_count": 3,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "Installing the correct packages:\n",
- "\n",
- "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
- ],
- "metadata": {
- "id": "jKxGok-ElYQ7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "!apt install python-opengl\n",
- "!apt install ffmpeg\n",
- "!apt install xvfb\n",
- "!apt install swig"
- ],
- "metadata": {
- "id": "nn6EETTc2Ewf"
- },
- "execution_count": 4,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "%cd /content/rl-algo-impls\n",
- "!pip install -r colab_requirements.txt"
- ],
- "metadata": {
- "id": "AfZh9rH3yQii"
- },
- "execution_count": 5,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Run Once Per Runtime"
- ],
- "metadata": {
- "id": "4o5HOLjc4wq7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "import wandb\n",
- "wandb.login()"
- ],
- "metadata": {
- "id": "PCXa5tdS2qFX"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Restart Session beteween runs"
- ],
- "metadata": {
- "id": "AZBZfSUV43JQ"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "from pyvirtualdisplay import Display\n",
- "\n",
- "virtual_display = Display(visible=0, size=(1400, 900))\n",
- "virtual_display.start()"
- ],
- "metadata": {
- "id": "VzemeQJP2NO9"
- },
- "execution_count": 7,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "%cd /content/rl-algo-impls\n",
- "!python enjoy.py --wandb-run-path={WANDB_RUN_PATH}"
- ],
- "metadata": {
- "id": "07aHYFH1zfXa"
- },
- "execution_count": null,
- "outputs": []
- }
- ]
-}
\ No newline at end of file
diff --git a/colab_requirements.txt b/colab_requirements.txt
deleted file mode 100644
index 798b0a43c5831756303e88f525d1f54bf3384851..0000000000000000000000000000000000000000
--- a/colab_requirements.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-AutoROM.accept-rom-license >= 0.4.2, < 0.5
-stable-baselines3[extra] >= 1.7.0, < 1.8
-gym[box2d] >= 0.21.0, < 0.22
-pyglet == 1.5.27
-wandb >= 0.13.10, < 0.14
-pyvirtualdisplay == 3.0
-pybullet >= 3.2.5, < 3.3
-tabulate >= 0.9.0, < 0.10
-huggingface-hub >= 0.12.0, < 0.13
-numexpr >= 2.8.4, < 2.9
-gym3 >= 0.3.3, < 0.4
-glfw >= 1.12.0, < 1.13
-procgen >= 0.10.7, < 0.11
-ipython >= 8.10.0, < 8.11
\ No newline at end of file
diff --git a/colab_train.ipynb b/colab_train.ipynb
deleted file mode 100644
index 7f981493d0545cfabd5b73db13f1f3bb30114855..0000000000000000000000000000000000000000
--- a/colab_train.ipynb
+++ /dev/null
@@ -1,200 +0,0 @@
-{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
- "colab": {
- "provenance": [],
- "machine_shape": "hm",
- "authorship_tag": "ABX9TyMmemQnx6G7GOnn6XBdjgxY",
- "include_colab_link": true
- },
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
- },
- "language_info": {
- "name": "python"
- },
- "gpuClass": "standard",
- "accelerator": "GPU"
- },
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "# [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) in Google Colaboratory\n",
- "## Parameters\n",
- "\n",
- "\n",
- "1. Wandb\n",
- "\n"
- ],
- "metadata": {
- "id": "S-tXDWP8WTLc"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from getpass import getpass\n",
- "import os\n",
- "os.environ[\"WANDB_API_KEY\"] = getpass(\"Wandb API key to upload metrics, videos, and models: \")"
- ],
- "metadata": {
- "id": "1ZtdYgxWNGwZ"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "2. train run parameters"
- ],
- "metadata": {
- "id": "ao0nAh3MOdN7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "ALGO = \"ppo\"\n",
- "ENV = \"CartPole-v1\"\n",
- "SEED = 1"
- ],
- "metadata": {
- "id": "jKL_NFhVOjSc"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Setup\n",
- "Clone [sgoodfriend/rl-algo-impls](https://github.com/sgoodfriend/rl-algo-impls) "
- ],
- "metadata": {
- "id": "bsG35Io0hmKG"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "!git clone https://github.com/sgoodfriend/rl-algo-impls.git"
- ],
- "metadata": {
- "id": "k5ynTV25hdAf"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "Installing the correct packages:\n",
- "\n",
- "While conda and poetry are generally used for package management, the mismatch in Python versions (3.10 in the project file vs 3.8 in Colab) makes using the package yml files difficult to use. For now, instead I'm going to specify the list of requirements manually below:"
- ],
- "metadata": {
- "id": "jKxGok-ElYQ7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "!apt install python-opengl\n",
- "!apt install ffmpeg\n",
- "!apt install xvfb\n",
- "!apt install swig"
- ],
- "metadata": {
- "id": "nn6EETTc2Ewf"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "%cd /content/rl-algo-impls\n",
- "!pip install -r colab_requirements.txt"
- ],
- "metadata": {
- "id": "AfZh9rH3yQii"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Run Once Per Runtime"
- ],
- "metadata": {
- "id": "4o5HOLjc4wq7"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "import wandb\n",
- "wandb.login()"
- ],
- "metadata": {
- "id": "PCXa5tdS2qFX"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Restart Session beteween runs"
- ],
- "metadata": {
- "id": "AZBZfSUV43JQ"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%%capture\n",
- "from pyvirtualdisplay import Display\n",
- "\n",
- "virtual_display = Display(visible=0, size=(1400, 900))\n",
- "virtual_display.start()"
- ],
- "metadata": {
- "id": "VzemeQJP2NO9"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "%cd /content/rl-algo-impls\n",
- "!python train.py --algo {ALGO} --env {ENV} --seed {SEED}"
- ],
- "metadata": {
- "id": "07aHYFH1zfXa"
- },
- "execution_count": null,
- "outputs": []
- }
- ]
-}
\ No newline at end of file
diff --git a/dqn/dqn.py b/dqn/dqn.py
deleted file mode 100644
index 741208430475c92980a730e4f88332dfb6c5254b..0000000000000000000000000000000000000000
--- a/dqn/dqn.py
+++ /dev/null
@@ -1,182 +0,0 @@
-import copy
-import numpy as np
-import random
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from collections import deque
-from torch.optim import Adam
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import List, NamedTuple, Optional, TypeVar
-
-from dqn.policy import DQNPolicy
-from shared.algorithm import Algorithm
-from shared.callbacks.callback import Callback
-from shared.schedule import linear_schedule
-
-
-class Transition(NamedTuple):
- obs: np.ndarray
- action: np.ndarray
- reward: float
- done: bool
- next_obs: np.ndarray
-
-
-class Batch(NamedTuple):
- obs: np.ndarray
- actions: np.ndarray
- rewards: np.ndarray
- dones: np.ndarray
- next_obs: np.ndarray
-
-
-class ReplayBuffer:
- def __init__(self, num_envs: int, maxlen: int) -> None:
- self.num_envs = num_envs
- self.buffer = deque(maxlen=maxlen)
-
- def add(
- self,
- obs: VecEnvObs,
- action: np.ndarray,
- reward: np.ndarray,
- done: np.ndarray,
- next_obs: VecEnvObs,
- ) -> None:
- assert isinstance(obs, np.ndarray)
- assert isinstance(next_obs, np.ndarray)
- for i in range(self.num_envs):
- self.buffer.append(
- Transition(obs[i], action[i], reward[i], done[i], next_obs[i])
- )
-
- def sample(self, batch_size: int) -> Batch:
- ts = random.sample(self.buffer, batch_size)
- return Batch(
- obs=np.array([t.obs for t in ts]),
- actions=np.array([t.action for t in ts]),
- rewards=np.array([t.reward for t in ts]),
- dones=np.array([t.done for t in ts]),
- next_obs=np.array([t.next_obs for t in ts]),
- )
-
- def __len__(self) -> int:
- return len(self.buffer)
-
-
-DQNSelf = TypeVar("DQNSelf", bound="DQN")
-
-
-class DQN(Algorithm):
- def __init__(
- self,
- policy: DQNPolicy,
- env: VecEnv,
- device: torch.device,
- tb_writer: SummaryWriter,
- learning_rate: float = 1e-4,
- buffer_size: int = 1_000_000,
- learning_starts: int = 50_000,
- batch_size: int = 32,
- tau: float = 1.0,
- gamma: float = 0.99,
- train_freq: int = 4,
- gradient_steps: int = 1,
- target_update_interval: int = 10_000,
- exploration_fraction: float = 0.1,
- exploration_initial_eps: float = 1.0,
- exploration_final_eps: float = 0.05,
- max_grad_norm: float = 10.0,
- ) -> None:
- super().__init__(policy, env, device, tb_writer)
- self.policy = policy
-
- self.optimizer = Adam(self.policy.q_net.parameters(), lr=learning_rate)
-
- self.target_q_net = copy.deepcopy(self.policy.q_net).to(self.device)
- self.target_q_net.train(False)
- self.tau = tau
- self.target_update_interval = target_update_interval
-
- self.replay_buffer = ReplayBuffer(self.env.num_envs, buffer_size)
- self.batch_size = batch_size
-
- self.learning_starts = learning_starts
- self.train_freq = train_freq
- self.gradient_steps = gradient_steps
-
- self.gamma = gamma
- self.exploration_eps_schedule = linear_schedule(
- exploration_initial_eps,
- exploration_final_eps,
- end_fraction=exploration_fraction,
- )
-
- self.max_grad_norm = max_grad_norm
-
- def learn(
- self: DQNSelf, total_timesteps: int, callback: Optional[Callback] = None
- ) -> DQNSelf:
- self.policy.train(True)
- obs = self.env.reset()
- obs = self._collect_rollout(self.learning_starts, obs, 1)
- learning_steps = total_timesteps - self.learning_starts
- timesteps_elapsed = 0
- steps_since_target_update = 0
- while timesteps_elapsed < learning_steps:
- progress = timesteps_elapsed / learning_steps
- eps = self.exploration_eps_schedule(progress)
- obs = self._collect_rollout(self.train_freq, obs, eps)
- rollout_steps = self.train_freq
- timesteps_elapsed += rollout_steps
- for _ in range(
- self.gradient_steps if self.gradient_steps > 0 else self.train_freq
- ):
- self.train()
- steps_since_target_update += rollout_steps
- if steps_since_target_update >= self.target_update_interval:
- self._update_target()
- steps_since_target_update = 0
- if callback:
- callback.on_step(timesteps_elapsed=rollout_steps)
- return self
-
- def train(self) -> None:
- if len(self.replay_buffer) < self.batch_size:
- return
- o, a, r, d, next_o = self.replay_buffer.sample(self.batch_size)
- o = torch.as_tensor(o, device=self.device)
- a = torch.as_tensor(a, device=self.device).unsqueeze(1)
- r = torch.as_tensor(r, dtype=torch.float32, device=self.device)
- d = torch.as_tensor(d, dtype=torch.long, device=self.device)
- next_o = torch.as_tensor(next_o, device=self.device)
-
- with torch.no_grad():
- target = r + (1 - d) * self.gamma * self.target_q_net(next_o).max(1).values
- current = self.policy.q_net(o).gather(dim=1, index=a).squeeze(1)
- loss = F.smooth_l1_loss(current, target)
-
- self.optimizer.zero_grad()
- loss.backward()
- if self.max_grad_norm:
- nn.utils.clip_grad_norm_(self.policy.q_net.parameters(), self.max_grad_norm)
- self.optimizer.step()
-
- def _collect_rollout(self, timesteps: int, obs: VecEnvObs, eps: float) -> VecEnvObs:
- for _ in range(0, timesteps, self.env.num_envs):
- action = self.policy.act(obs, eps, deterministic=False)
- next_obs, reward, done, _ = self.env.step(action)
- self.replay_buffer.add(obs, action, reward, done, next_obs)
- obs = next_obs
- return obs
-
- def _update_target(self) -> None:
- for target_param, param in zip(
- self.target_q_net.parameters(), self.policy.q_net.parameters()
- ):
- target_param.data.copy_(
- self.tau * param.data + (1 - self.tau) * target_param.data
- )
diff --git a/dqn/policy.py b/dqn/policy.py
deleted file mode 100644
index 6bd31e4beeb1d5922e3cad6713aa05a9e0831f24..0000000000000000000000000000000000000000
--- a/dqn/policy.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import numpy as np
-import os
-import torch
-
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from typing import Optional, Sequence, TypeVar
-
-from dqn.q_net import QNetwork
-from shared.policy.policy import Policy
-
-DQNPolicySelf = TypeVar("DQNPolicySelf", bound="DQNPolicy")
-
-
-class DQNPolicy(Policy):
- def __init__(
- self,
- env: VecEnv,
- hidden_sizes: Sequence[int] = [],
- cnn_feature_dim: int = 512,
- cnn_style: str = "nature",
- cnn_layers_init_orthogonal: Optional[bool] = None,
- **kwargs,
- ) -> None:
- super().__init__(env, **kwargs)
- self.q_net = QNetwork(
- env.observation_space,
- env.action_space,
- hidden_sizes,
- cnn_feature_dim=cnn_feature_dim,
- cnn_style=cnn_style,
- cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
- )
-
- def act(
- self, obs: VecEnvObs, eps: float = 0, deterministic: bool = True
- ) -> np.ndarray:
- assert eps == 0 if deterministic else eps >= 0
- if not deterministic and np.random.random() < eps:
- return np.array(
- [self.env.action_space.sample() for _ in range(self.env.num_envs)]
- )
- else:
- o = self._as_tensor(obs)
- with torch.no_grad():
- return self.q_net(o).argmax(axis=1).cpu().numpy()
diff --git a/dqn/q_net.py b/dqn/q_net.py
deleted file mode 100644
index 200e83c09bb20fbdc479ad5900381c859a778035..0000000000000000000000000000000000000000
--- a/dqn/q_net.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import gym
-import torch as th
-import torch.nn as nn
-
-from gym.spaces import Discrete
-from typing import Optional, Sequence, Type
-
-from shared.module.feature_extractor import FeatureExtractor
-from shared.module.module import mlp
-
-
-class QNetwork(nn.Module):
- def __init__(
- self,
- observation_space: gym.Space,
- action_space: gym.Space,
- hidden_sizes: Sequence[int] = [],
- activation: Type[nn.Module] = nn.ReLU, # Used by stable-baselines3
- cnn_feature_dim: int = 512,
- cnn_style: str = "nature",
- cnn_layers_init_orthogonal: Optional[bool] = None,
- ) -> None:
- super().__init__()
- assert isinstance(action_space, Discrete)
- self._feature_extractor = FeatureExtractor(
- observation_space,
- activation,
- cnn_feature_dim=cnn_feature_dim,
- cnn_style=cnn_style,
- cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
- )
- layer_sizes = (
- (self._feature_extractor.out_dim,) + tuple(hidden_sizes) + (action_space.n,)
- )
- self._fc = mlp(layer_sizes, activation)
-
- def forward(self, obs: th.Tensor) -> th.Tensor:
- x = self._feature_extractor(obs)
- return self._fc(x)
diff --git a/hf-deep-rl/dqn_SpaceInvadersNoFrameskip_v4.ipynb b/hf-deep-rl/dqn_SpaceInvadersNoFrameskip_v4.ipynb
deleted file mode 100644
index 72123293ef9ed830512f103fa84ba46cf55604da..0000000000000000000000000000000000000000
--- a/hf-deep-rl/dqn_SpaceInvadersNoFrameskip_v4.ipynb
+++ /dev/null
@@ -1,7126 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "YdQPrKjkMhlp"
- },
- "outputs": [],
- "source": [
- "ALGO = \"dqn\"\n",
- "ENV = \"SpaceInvadersNoFrameskip-v4\"\n",
- "IS_EVAL_DETERMINISTIC = True\n",
- "MODEL_NAME = f\"{ALGO}-sb3-{ENV}\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "RgQFeu858QEi"
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "!apt install python-opengl\n",
- "!apt install ffmpeg\n",
- "!apt install xvfb\n",
- "!apt install swig\n",
- "!apt-get install swig cmake freeglut3-dev"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "BcMKMQiV8uqv"
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "!pip install stable-baselines3[extra] box2d huggingface_sb3 pyglet==1.5.1 pyvirtualdisplay==3.0"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "pnSOaJfd8oCh"
- },
- "outputs": [],
- "source": [
- "%%capture\n",
- "from pyvirtualdisplay import Display\n",
- "\n",
- "virtual_display = Display(visible=0, size=(1400, 900))\n",
- "virtual_display.start()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "wF3U_U5JM_JP",
- "outputId": "9b591dd9-72e5-477f-9151-51b3574fb7b2"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
- " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
- " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
- " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
- " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
- " \n",
- " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
- "Token: \n",
- "Add token as git credential? (Y/n) \n",
- "Token is valid.\n",
- "Your token has been saved in your configured git credential helpers (store).\n",
- "Your token has been saved to /root/.cache/huggingface/token\n",
- "Login successful\n"
- ]
- }
- ],
- "source": [
- "!git config --global credential.helper store\n",
- "!huggingface-cli login"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "aENGEenlE5A3",
- "outputId": "cdecc60c-862d-48d4-ab18-204e0e5d301b"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00559 |\n",
- "| n_updates | 293223 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.58e+03 |\n",
- "| ep_rew_mean | 550 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39960 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13491 |\n",
- "| total_timesteps | 9584512 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0384 |\n",
- "| n_updates | 293265 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.58e+03 |\n",
- "| ep_rew_mean | 548 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39964 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13493 |\n",
- "| total_timesteps | 9585952 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0247 |\n",
- "| n_updates | 293310 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.6e+03 |\n",
- "| ep_rew_mean | 557 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39968 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13497 |\n",
- "| total_timesteps | 9588440 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0276 |\n",
- "| n_updates | 293388 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.6e+03 |\n",
- "| ep_rew_mean | 557 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39972 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13498 |\n",
- "| total_timesteps | 9589496 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00934 |\n",
- "| n_updates | 293421 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.59e+03 |\n",
- "| ep_rew_mean | 555 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39976 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13500 |\n",
- "| total_timesteps | 9590760 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0709 |\n",
- "| n_updates | 293461 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.57e+03 |\n",
- "| ep_rew_mean | 554 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39980 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13501 |\n",
- "| total_timesteps | 9591464 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0103 |\n",
- "| n_updates | 293483 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.55e+03 |\n",
- "| ep_rew_mean | 549 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39984 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13502 |\n",
- "| total_timesteps | 9592776 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00816 |\n",
- "| n_updates | 293524 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.55e+03 |\n",
- "| ep_rew_mean | 548 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39988 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13503 |\n",
- "| total_timesteps | 9593536 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0543 |\n",
- "| n_updates | 293547 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.53e+03 |\n",
- "| ep_rew_mean | 546 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39992 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13505 |\n",
- "| total_timesteps | 9594496 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00431 |\n",
- "| n_updates | 293577 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.52e+03 |\n",
- "| ep_rew_mean | 547 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 39996 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13507 |\n",
- "| total_timesteps | 9596000 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00963 |\n",
- "| n_updates | 293624 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.52e+03 |\n",
- "| ep_rew_mean | 547 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40000 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13508 |\n",
- "| total_timesteps | 9597216 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0125 |\n",
- "| n_updates | 293662 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.52e+03 |\n",
- "| ep_rew_mean | 545 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40004 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13510 |\n",
- "| total_timesteps | 9598552 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00645 |\n",
- "| n_updates | 293704 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.52e+03 |\n",
- "| ep_rew_mean | 543 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40008 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13512 |\n",
- "| total_timesteps | 9600088 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00603 |\n",
- "| n_updates | 293752 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.54e+03 |\n",
- "| ep_rew_mean | 545 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40012 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13513 |\n",
- "| total_timesteps | 9600896 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00729 |\n",
- "| n_updates | 293777 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.54e+03 |\n",
- "| ep_rew_mean | 544 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40016 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13515 |\n",
- "| total_timesteps | 9602128 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0898 |\n",
- "| n_updates | 293816 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.55e+03 |\n",
- "| ep_rew_mean | 545 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40020 |\n",
- "| fps | 710 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "| n_updates | 294320 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.55e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.55e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.54e+03 |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "| n_updates | 294460 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.51e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 294502 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.48e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 294522 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.46e+03 |\n",
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- "| episodes | 40096 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.41e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.41e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.41e+03 |\n",
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- "| episodes | 40124 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
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- "| episodes | 40128 |\n",
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- "| train/ | |\n",
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- "| n_updates | 294892 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.34e+03 |\n",
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- "| episodes | 40132 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 294942 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.35e+03 |\n",
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- "| episodes | 40136 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0262 |\n",
- "| n_updates | 294965 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.36e+03 |\n",
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- "| episodes | 40140 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295020 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.36e+03 |\n",
- "| ep_rew_mean | 528 |\n",
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- "| episodes | 40144 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0314 |\n",
- "| n_updates | 295078 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.36e+03 |\n",
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- "| episodes | 40148 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295114 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.37e+03 |\n",
- "| ep_rew_mean | 530 |\n",
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- "| episodes | 40152 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295147 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.39e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40156 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295175 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40160 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295242 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.38e+03 |\n",
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- "| episodes | 40164 |\n",
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- "| train/ | |\n",
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- "| n_updates | 295279 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.38e+03 |\n",
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- "| episodes | 40168 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295304 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.37e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40172 |\n",
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- "| total_timesteps | 9650840 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295338 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.39e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40176 |\n",
- "| fps | 710 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295389 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.39e+03 |\n",
- "| ep_rew_mean | 525 |\n",
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- "| time/ | |\n",
- "| episodes | 40180 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13584 |\n",
- "| total_timesteps | 9652872 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0193 |\n",
- "| n_updates | 295402 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "| n_updates | 295435 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295481 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40192 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| time/ | |\n",
- "| episodes | 40196 |\n",
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- "| train/ | |\n",
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- "| n_updates | 295533 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40200 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.39e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40204 |\n",
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- "| train/ | |\n",
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- "| n_updates | 295579 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.38e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40208 |\n",
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- "| train/ | |\n",
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- "| n_updates | 295620 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.39e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40212 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295670 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.36e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40216 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0117 |\n",
- "| n_updates | 295685 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.35e+03 |\n",
- "| ep_rew_mean | 522 |\n",
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- "| time/ | |\n",
- "| episodes | 40220 |\n",
- "| fps | 710 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0115 |\n",
- "| n_updates | 295729 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.33e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40224 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295762 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.34e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40228 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295783 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.33e+03 |\n",
- "| ep_rew_mean | 516 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40232 |\n",
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- "| train/ | |\n",
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- "| loss | 0.0291 |\n",
- "| n_updates | 295826 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40236 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0423 |\n",
- "| n_updates | 295858 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.32e+03 |\n",
- "| ep_rew_mean | 511 |\n",
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- "| time/ | |\n",
- "| episodes | 40240 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0529 |\n",
- "| n_updates | 295894 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
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- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40244 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 295946 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.3e+03 |\n",
- "| ep_rew_mean | 503 |\n",
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- "| time/ | |\n",
- "| episodes | 40248 |\n",
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- "| train/ | |\n",
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- "| n_updates | 295985 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.3e+03 |\n",
- "| ep_rew_mean | 504 |\n",
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- "| time/ | |\n",
- "| episodes | 40252 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 296000 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.3e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40256 |\n",
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- "| train/ | |\n",
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- "| loss | 0.025 |\n",
- "| n_updates | 296064 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.3e+03 |\n",
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- "| episodes | 40260 |\n",
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- "| train/ | |\n",
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- "| n_updates | 296083 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.32e+03 |\n",
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- "| episodes | 40264 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
- "| ep_rew_mean | 499 |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.32e+03 |\n",
- "| ep_rew_mean | 497 |\n",
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- "| time/ | |\n",
- "| episodes | 40280 |\n",
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- "| train/ | |\n",
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- "| n_updates | 296281 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40284 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0292 |\n",
- "| n_updates | 296308 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
- "| ep_rew_mean | 494 |\n",
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- "| time/ | |\n",
- "| episodes | 40288 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0116 |\n",
- "| n_updates | 296333 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
- "| ep_rew_mean | 488 |\n",
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- "| time/ | |\n",
- "| episodes | 40292 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0118 |\n",
- "| n_updates | 296353 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.27e+03 |\n",
- "| ep_rew_mean | 486 |\n",
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- "| time/ | |\n",
- "| episodes | 40296 |\n",
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- "| total_timesteps | 9684016 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00861 |\n",
- "| n_updates | 296375 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.27e+03 |\n",
- "| ep_rew_mean | 488 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40300 |\n",
- "| fps | 710 |\n",
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- "| total_timesteps | 9684720 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.009 |\n",
- "| n_updates | 296397 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
- "| ep_rew_mean | 488 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40304 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13630 |\n",
- "| total_timesteps | 9686304 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0261 |\n",
- "| n_updates | 296446 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
- "| ep_rew_mean | 488 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40308 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13632 |\n",
- "| total_timesteps | 9687432 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00495 |\n",
- "| n_updates | 296482 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
- "| ep_rew_mean | 485 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40312 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13633 |\n",
- "| total_timesteps | 9688664 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0229 |\n",
- "| n_updates | 296520 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
- "| ep_rew_mean | 485 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40316 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13635 |\n",
- "| total_timesteps | 9689648 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0215 |\n",
- "| n_updates | 296551 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
- "| ep_rew_mean | 485 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40320 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13636 |\n",
- "| total_timesteps | 9690440 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0166 |\n",
- "| n_updates | 296576 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
- "| ep_rew_mean | 482 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40324 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13638 |\n",
- "| total_timesteps | 9691808 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0308 |\n",
- "| n_updates | 296618 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
- "| ep_rew_mean | 484 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40328 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13639 |\n",
- "| total_timesteps | 9692624 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00627 |\n",
- "| n_updates | 296644 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
- "| ep_rew_mean | 482 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40332 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13641 |\n",
- "| total_timesteps | 9693952 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0244 |\n",
- "| n_updates | 296685 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
- "| ep_rew_mean | 483 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40336 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13642 |\n",
- "| total_timesteps | 9694632 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00614 |\n",
- "| n_updates | 296707 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.21e+03 |\n",
- "| ep_rew_mean | 477 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40340 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13643 |\n",
- "| total_timesteps | 9695784 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.035 |\n",
- "| n_updates | 296743 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.2e+03 |\n",
- "| ep_rew_mean | 476 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40344 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13645 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.2e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.19e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.24e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.24e+03 |\n",
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- "| episodes | 40400 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 297445 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.2e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.21e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.21e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.19e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.18e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.21e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.2e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.2e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 297777 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.2e+03 |\n",
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- "| episodes | 40456 |\n",
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- "| train/ | |\n",
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- "| n_updates | 297818 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.17e+03 |\n",
- "| ep_rew_mean | 478 |\n",
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- "| time/ | |\n",
- "| episodes | 40460 |\n",
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- "| train/ | |\n",
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- "| n_updates | 297839 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.18e+03 |\n",
- "| ep_rew_mean | 479 |\n",
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- "| episodes | 40464 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 297876 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.15e+03 |\n",
- "| ep_rew_mean | 476 |\n",
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- "| time/ | |\n",
- "| episodes | 40468 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.022 |\n",
- "| n_updates | 297937 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.16e+03 |\n",
- "| ep_rew_mean | 476 |\n",
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- "| time/ | |\n",
- "| episodes | 40472 |\n",
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- "| train/ | |\n",
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- "| loss | 0.0524 |\n",
- "| n_updates | 297979 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.16e+03 |\n",
- "| ep_rew_mean | 475 |\n",
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- "| time/ | |\n",
- "| episodes | 40476 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 298026 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.18e+03 |\n",
- "| ep_rew_mean | 478 |\n",
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- "| time/ | |\n",
- "| episodes | 40480 |\n",
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- "| total_timesteps | 9737616 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00947 |\n",
- "| n_updates | 298050 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.21e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40484 |\n",
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- "| total_timesteps | 9738368 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0125 |\n",
- "| n_updates | 298073 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.21e+03 |\n",
- "| ep_rew_mean | 487 |\n",
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- "| time/ | |\n",
- "| episodes | 40488 |\n",
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- "| total_timesteps | 9740528 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0113 |\n",
- "| n_updates | 298141 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.22e+03 |\n",
- "| ep_rew_mean | 489 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40492 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0309 |\n",
- "| n_updates | 298215 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.22e+03 |\n",
- "| ep_rew_mean | 492 |\n",
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- "| time/ | |\n",
- "| episodes | 40496 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 298238 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
- "| ep_rew_mean | 494 |\n",
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- "| time/ | |\n",
- "| episodes | 40500 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.101 |\n",
- "| n_updates | 298261 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
- "| ep_rew_mean | 494 |\n",
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- "| time/ | |\n",
- "| episodes | 40504 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 298296 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
- "| ep_rew_mean | 494 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.26e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 298690 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.27e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.23e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 298734 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.24e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 298794 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.24e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 298833 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.25e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 298860 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.27e+03 |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
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- "| n_updates | 298893 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.27e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 298944 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.29e+03 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 298997 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.29e+03 |\n",
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- "| episodes | 40580 |\n",
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- "| train/ | |\n",
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- "| n_updates | 299030 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.29e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40584 |\n",
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- "| train/ | |\n",
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- "| n_updates | 299054 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.29e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40588 |\n",
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- "| train/ | |\n",
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- "| loss | 0.0211 |\n",
- "| n_updates | 299075 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.3e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40592 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 299170 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.31e+03 |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
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- "| n_updates | 299196 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.32e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40600 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 299270 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.32e+03 |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 299292 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.35e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40608 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 299336 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.37e+03 |\n",
- "| ep_rew_mean | 540 |\n",
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- "| time/ | |\n",
- "| episodes | 40612 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.37e+03 |\n",
- "| ep_rew_mean | 540 |\n",
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- "| episodes | 40616 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 299390 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.38e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40620 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 299445 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.41e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40624 |\n",
- "| fps | 710 |\n",
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- "| total_timesteps | 9783160 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0088 |\n",
- "| n_updates | 299473 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
- "| ep_rew_mean | 544 |\n",
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- "| time/ | |\n",
- "| episodes | 40628 |\n",
- "| fps | 710 |\n",
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- "| total_timesteps | 9784936 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0394 |\n",
- "| n_updates | 299529 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.42e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40632 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0137 |\n",
- "| n_updates | 299631 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
- "| ep_rew_mean | 545 |\n",
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- "| episodes | 40636 |\n",
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- "| total_timesteps | 9788592 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0297 |\n",
- "| n_updates | 299643 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
- "| ep_rew_mean | 544 |\n",
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- "| time/ | |\n",
- "| episodes | 40640 |\n",
- "| fps | 710 |\n",
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- "| total_timesteps | 9789528 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0119 |\n",
- "| n_updates | 299672 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 40644 |\n",
- "| fps | 710 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0411 |\n",
- "| n_updates | 299698 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.42e+03 |\n",
- "| ep_rew_mean | 543 |\n",
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- "| time/ | |\n",
- "| episodes | 40648 |\n",
- "| fps | 710 |\n",
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- "| total_timesteps | 9791720 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0575 |\n",
- "| n_updates | 299741 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.42e+03 |\n",
- "| ep_rew_mean | 543 |\n",
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- "| time/ | |\n",
- "| episodes | 40652 |\n",
- "| fps | 710 |\n",
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- "| total_timesteps | 9792656 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0116 |\n",
- "| n_updates | 299770 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
- "| ep_rew_mean | 540 |\n",
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- "| time/ | |\n",
- "| episodes | 40656 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13775 |\n",
- "| total_timesteps | 9793248 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0185 |\n",
- "| n_updates | 299788 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
- "| ep_rew_mean | 538 |\n",
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- "| time/ | |\n",
- "| episodes | 40660 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13776 |\n",
- "| total_timesteps | 9793968 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00565 |\n",
- "| n_updates | 299811 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.39e+03 |\n",
- "| ep_rew_mean | 536 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40664 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13778 |\n",
- "| total_timesteps | 9795304 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0329 |\n",
- "| n_updates | 299853 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
- "| ep_rew_mean | 536 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 40668 |\n",
- "| fps | 710 |\n",
- "| time_elapsed | 13781 |\n",
- "| total_timesteps | 9797216 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.017 |\n",
- "| n_updates | 299912 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.4e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.41e+03 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.42e+03 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.42e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.46e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.46e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.46e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.46e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.45e+03 |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.43e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 301047 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.45e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 301091 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.45e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.45e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.42e+03 |\n",
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- "| train/ | |\n",
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- "| loss | 0.02 |\n",
- "| n_updates | 301280 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
- "| ep_rew_mean | 540 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 301309 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 301342 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 301389 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.45e+03 |\n",
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- "| episodes | 40828 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 301430 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.44e+03 |\n",
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- "| time/ | |\n",
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- "----------------------------------\n",
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- "| rollout/ | |\n",
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- "----------------------------------\n",
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- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
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- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.52e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.54e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.57e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.57e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.57e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.6e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 303030 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.61e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.61e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| time/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.62e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.62e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.62e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.65e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.68e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.67e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.7e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.7e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.75e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.76e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
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- "| train/ | |\n",
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- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 304512 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0128 |\n",
- "| n_updates | 304574 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.73e+03 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 304602 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.73e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 304633 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
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- "| train/ | |\n",
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- "| n_updates | 304700 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
- "| ep_rew_mean | 596 |\n",
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- "| train/ | |\n",
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- "| n_updates | 304747 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
- "| ep_rew_mean | 596 |\n",
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- "| episodes | 41140 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 304792 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.72e+03 |\n",
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- "| time/ | |\n",
- "| episodes | 41144 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
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- "| n_updates | 304824 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.7e+03 |\n",
- "| ep_rew_mean | 584 |\n",
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- "| time/ | |\n",
- "| episodes | 41148 |\n",
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- "| time_elapsed | 13994 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0337 |\n",
- "| n_updates | 304843 |\n",
- "----------------------------------\n",
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- "| rollout/ | |\n",
- "| ep_len_mean | 3.68e+03 |\n",
- "| ep_rew_mean | 581 |\n",
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- "| time/ | |\n",
- "| episodes | 41152 |\n",
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- "| time_elapsed | 13995 |\n",
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- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.029 |\n",
- "| n_updates | 304879 |\n",
- "----------------------------------\n",
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- "| rollout/ | |\n",
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- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41156 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 13997 |\n",
- "| total_timesteps | 9957560 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0209 |\n",
- "| n_updates | 304923 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
- "| ep_rew_mean | 585 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41160 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14000 |\n",
- "| total_timesteps | 9959432 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0204 |\n",
- "| n_updates | 304982 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
- "| ep_rew_mean | 584 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41164 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14002 |\n",
- "| total_timesteps | 9960832 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0136 |\n",
- "| n_updates | 305025 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
- "| ep_rew_mean | 585 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41168 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14003 |\n",
- "| total_timesteps | 9962064 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00879 |\n",
- "| n_updates | 305064 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.68e+03 |\n",
- "| ep_rew_mean | 581 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41172 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14005 |\n",
- "| total_timesteps | 9963528 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.134 |\n",
- "| n_updates | 305110 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.71e+03 |\n",
- "| ep_rew_mean | 584 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41176 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14008 |\n",
- "| total_timesteps | 9965704 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0127 |\n",
- "| n_updates | 305178 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.72e+03 |\n",
- "| ep_rew_mean | 588 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41180 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14010 |\n",
- "| total_timesteps | 9967040 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0373 |\n",
- "| n_updates | 305219 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.75e+03 |\n",
- "| ep_rew_mean | 595 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41184 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14012 |\n",
- "| total_timesteps | 9968696 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.01 |\n",
- "| n_updates | 305271 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.77e+03 |\n",
- "| ep_rew_mean | 594 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41188 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14014 |\n",
- "| total_timesteps | 9970016 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0304 |\n",
- "| n_updates | 305312 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.77e+03 |\n",
- "| ep_rew_mean | 594 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41192 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14015 |\n",
- "| total_timesteps | 9970800 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0146 |\n",
- "| n_updates | 305337 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.77e+03 |\n",
- "| ep_rew_mean | 599 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41196 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14017 |\n",
- "| total_timesteps | 9972328 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0192 |\n",
- "| n_updates | 305385 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.76e+03 |\n",
- "| ep_rew_mean | 598 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41200 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14019 |\n",
- "| total_timesteps | 9973992 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0331 |\n",
- "| n_updates | 305437 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.76e+03 |\n",
- "| ep_rew_mean | 597 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41204 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14020 |\n",
- "| total_timesteps | 9974176 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00668 |\n",
- "| n_updates | 305442 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.76e+03 |\n",
- "| ep_rew_mean | 597 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41208 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14022 |\n",
- "| total_timesteps | 9975720 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0169 |\n",
- "| n_updates | 305491 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.75e+03 |\n",
- "| ep_rew_mean | 596 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41212 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14023 |\n",
- "| total_timesteps | 9976920 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0473 |\n",
- "| n_updates | 305528 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.74e+03 |\n",
- "| ep_rew_mean | 592 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41216 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14026 |\n",
- "| total_timesteps | 9978760 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.129 |\n",
- "| n_updates | 305586 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.72e+03 |\n",
- "| ep_rew_mean | 590 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41220 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14027 |\n",
- "| total_timesteps | 9979296 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.022 |\n",
- "| n_updates | 305602 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.73e+03 |\n",
- "| ep_rew_mean | 586 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41224 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14028 |\n",
- "| total_timesteps | 9980608 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0114 |\n",
- "| n_updates | 305643 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.73e+03 |\n",
- "| ep_rew_mean | 586 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41228 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14030 |\n",
- "| total_timesteps | 9981792 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.00616 |\n",
- "| n_updates | 305680 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.71e+03 |\n",
- "| ep_rew_mean | 579 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41232 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14031 |\n",
- "| total_timesteps | 9982136 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0352 |\n",
- "| n_updates | 305691 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
- "| ep_rew_mean | 577 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41236 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14032 |\n",
- "| total_timesteps | 9983392 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0245 |\n",
- "| n_updates | 305730 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.7e+03 |\n",
- "| ep_rew_mean | 580 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41240 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14034 |\n",
- "| total_timesteps | 9985096 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.12 |\n",
- "| n_updates | 305784 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
- "| ep_rew_mean | 579 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41244 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14036 |\n",
- "| total_timesteps | 9986336 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.012 |\n",
- "| n_updates | 305822 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.69e+03 |\n",
- "| ep_rew_mean | 581 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41248 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14038 |\n",
- "| total_timesteps | 9987568 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0575 |\n",
- "| n_updates | 305861 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.68e+03 |\n",
- "| ep_rew_mean | 579 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41252 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14040 |\n",
- "| total_timesteps | 9988832 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0057 |\n",
- "| n_updates | 305900 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.68e+03 |\n",
- "| ep_rew_mean | 580 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41256 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14040 |\n",
- "| total_timesteps | 9989448 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0118 |\n",
- "| n_updates | 305920 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.67e+03 |\n",
- "| ep_rew_mean | 577 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41260 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14043 |\n",
- "| total_timesteps | 9991040 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0603 |\n",
- "| n_updates | 305969 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.68e+03 |\n",
- "| ep_rew_mean | 577 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41264 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14043 |\n",
- "| total_timesteps | 9991504 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0227 |\n",
- "| n_updates | 305984 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.64e+03 |\n",
- "| ep_rew_mean | 571 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41268 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14045 |\n",
- "| total_timesteps | 9992488 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0124 |\n",
- "| n_updates | 306015 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.65e+03 |\n",
- "| ep_rew_mean | 570 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41272 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14047 |\n",
- "| total_timesteps | 9993968 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0099 |\n",
- "| n_updates | 306061 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.65e+03 |\n",
- "| ep_rew_mean | 567 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41276 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14049 |\n",
- "| total_timesteps | 9995808 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0214 |\n",
- "| n_updates | 306118 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.63e+03 |\n",
- "| ep_rew_mean | 565 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41280 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14051 |\n",
- "| total_timesteps | 9996832 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.025 |\n",
- "| n_updates | 306150 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.64e+03 |\n",
- "| ep_rew_mean | 564 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41284 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14051 |\n",
- "| total_timesteps | 9997472 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0209 |\n",
- "| n_updates | 306170 |\n",
- "----------------------------------\n",
- "----------------------------------\n",
- "| rollout/ | |\n",
- "| ep_len_mean | 3.62e+03 |\n",
- "| ep_rew_mean | 556 |\n",
- "| exploration_rate | 0.01 |\n",
- "| time/ | |\n",
- "| episodes | 41288 |\n",
- "| fps | 711 |\n",
- "| time_elapsed | 14054 |\n",
- "| total_timesteps | 9999208 |\n",
- "| train/ | |\n",
- "| learning_rate | 0.0001 |\n",
- "| loss | 0.0163 |\n",
- "| n_updates | 306225 |\n",
- "----------------------------------\n"
- ]
- },
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "execution_count": 7
- }
- ],
- "source": [
- "from stable_baselines3 import DQN\n",
- "from stable_baselines3.common.atari_wrappers import AtariWrapper\n",
- "from stable_baselines3.common.env_util import make_vec_env\n",
- "\n",
- "def make_env(n_envs=1):\n",
- " return make_vec_env(ENV, n_envs=n_envs, wrapper_class=AtariWrapper)\n",
- "\n",
- "env = make_env(n_envs=8)\n",
- "\n",
- "model = DQN(\n",
- " \"CnnPolicy\",\n",
- " env,\n",
- " learning_rate=1e-4,\n",
- " buffer_size=400_000,\n",
- " learning_starts=200_000,\n",
- " batch_size=32,\n",
- " tau=1.0,\n",
- " gamma=0.99,\n",
- " train_freq=4,\n",
- " gradient_steps=1,\n",
- " replay_buffer_class=None,\n",
- " replay_buffer_kwargs={\"handle_timeout_termination\": False},\n",
- " optimize_memory_usage=True,\n",
- " target_update_interval=30_000,\n",
- " exploration_fraction=0.1,\n",
- " exploration_initial_eps=1.0,\n",
- " exploration_final_eps=0.01,\n",
- " verbose=1,\n",
- ")\n",
- "model.learn(total_timesteps=10_000_000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "vMliyZTFL3aV",
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "outputId": "d655fe88-04b2-495e-8d11-633c6c958167"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "698.0 +/- 166.63132958720578\n"
- ]
- }
- ],
- "source": [
- "from stable_baselines3.common.evaluation import evaluate_policy\n",
- "\n",
- "eval_env = make_env()\n",
- "mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=IS_EVAL_DETERMINISTIC)\n",
- "print(f\"{mean_reward} +/- {std_reward}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "H-HMy8-UM9Tk",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 338,
- "referenced_widgets": [
- "b13f44315b814221b8ac58f781b8d8dc",
- "8b8326558d46419d83174ae89b883b2b",
- "82f8442ac69b41b6b6a8065e547b03c7",
- "b81f4b6541f84458932d8eb0228e7ef8",
- "d5356d552ed9423195f870691079b629",
- "886117612d324dac9bbabeee8fc97636",
- "e1fbcd58494a45b2b880eb38577725fb",
- "5d437a24cfba48dbb4498f91c68ba0d4",
- "fb23dea0a15e458493c80a4a05bcb987",
- "025b9573447c44839be54206e8c72546",
- "9449cb09deee4433badf7f23d54066dd",
- "c631f3d7414a4c85b1d90b0a6081ae0d",
- "2f131b76e9a245a8a70eaecec3a79dab",
- "ec4732db7e4341dca509fa103064f234",
- "0ef1c5036b934de4833619982d793a6e",
- "6c17ac1c17fa4e859766a8e94ec8b9a5",
- "bedc1f14e2ca4c66b0dbc36f61c838ed",
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- ]
- },
- "outputId": "52e0f961-a52a-4f04-8809-eb48f8cb1039"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
- "create a model card and push everything to the hub. It might take up to 1min.\n",
- "This is a work in progress: if you encounter a bug, please open an issue.\u001b[0m\n",
- "Saving video to /tmp/tmpksnmhmmy/-step-0-to-step-1000.mp4\n",
- "\u001b[38;5;4mℹ Pushing repo sgoodfriend/dqn-sb3-SpaceInvadersNoFrameskip-v4 to the\n",
- "Hugging Face Hub\u001b[0m\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "pytorch_variables.pth: 0%| | 0.00/431 [00:00= 1.10.0, < 1.11
-tensorboard >= ^2.11.0, < 2.12
-AutoROM.accept-rom-license >= 0.4.2, < 0.5
-stable-baselines3[extra] >= 1.7.0, < 1.8
-gym[box2d] >= 0.21.0, < 0.22
-pyglet == 1.5.27
-wandb >= 0.13.10, < 0.14
-pyvirtualdisplay == 3.0
-pybullet >= 3.2.5, < 3.3
-tabulate >= 0.9.0, < 0.10
-huggingface-hub >= 0.12.0, < 0.13
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-glfw >= 1.12.0, < 1.13
-procgen >= 0.10.7, < 0.11
-ipython >= 8.10.0, < 8.11
\ No newline at end of file
diff --git a/lambda_labs/procgen_benchmark.sh b/lambda_labs/procgen_benchmark.sh
deleted file mode 100644
index 9e58f69ed4fcf6bbc58cca335ce3badbd9e3ca58..0000000000000000000000000000000000000000
--- a/lambda_labs/procgen_benchmark.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-source benchmarks/train_loop.sh
-
-# export WANDB_PROJECT_NAME="rl-algo-impls"
-
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-
-ALGOS=(
- # "vpg"
- # "dqn"
- "ppo"
-)
-ENVS=(
- "procgen-coinrun-easy"
- "procgen-starpilot-easy"
- "procgen-bossfight-easy"
- "procgen-bigfish-easy"
-)
-train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
diff --git a/lambda_labs/setup.sh b/lambda_labs/setup.sh
deleted file mode 100644
index 79d7f0a0b9bcc087b3b40065830bc8afef29ec18..0000000000000000000000000000000000000000
--- a/lambda_labs/setup.sh
+++ /dev/null
@@ -1,10 +0,0 @@
-sudo apt update
-sudo apt install -y python-opengl
-sudo apt install -y ffmpeg
-sudo apt install -y xvfb
-sudo apt install -y swig
-
-python3 -m pip install --upgrade pip
-pip install --upgrade torch torchvision torchaudio
-
-pip install --upgrade -r ~/rl-algo-impls/lambda_labs/lambda_requirements.txt
\ No newline at end of file
diff --git a/lambda_labs/starpilot_hard_benchmark.sh b/lambda_labs/starpilot_hard_benchmark.sh
deleted file mode 100644
index fce0bb5aebf468f566ce2dc8a7ca013f2e6180db..0000000000000000000000000000000000000000
--- a/lambda_labs/starpilot_hard_benchmark.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-source benchmarks/train_loop.sh
-
-# export WANDB_PROJECT_NAME="rl-algo-impls"
-
-BENCHMARK_MAX_PROCS="${BENCHMARK_MAX_PROCS:-3}"
-
-ALGOS=(
- # "vpg"
- # "dqn"
- "ppo"
-)
-ENVS=(
- "procgen-starpilot-hard"
- "procgen-starpilot-hard-2xIMPALA"
- "procgen-starpilot-hard-2xIMPALA-fat"
- "procgen-starpilot-hard-4xIMPALA"
-)
-train_loop "${ALGOS[*]}" "${ENVS[*]}" | xargs -I CMD -P $BENCHMARK_MAX_PROCS bash -c CMD
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index da115fe5e6e3297f30cd68c4a85ca151fe1414aa..0000000000000000000000000000000000000000
--- a/poetry.lock
+++ /dev/null
@@ -1,4466 +0,0 @@
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-
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diff --git a/ppo/policy.py b/ppo/policy.py
deleted file mode 100644
index c430708179be934b9ecfe0793ee97436e1937db1..0000000000000000000000000000000000000000
--- a/ppo/policy.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv
-from typing import Optional, Sequence
-
-from gym.spaces import Box, Discrete
-from shared.policy.on_policy import ActorCritic, default_hidden_sizes
-
-
-class PPOActorCritic(ActorCritic):
- def __init__(
- self,
- env: VecEnv,
- pi_hidden_sizes: Optional[Sequence[int]] = None,
- v_hidden_sizes: Optional[Sequence[int]] = None,
- **kwargs,
- ) -> None:
- pi_hidden_sizes = (
- pi_hidden_sizes
- if pi_hidden_sizes is not None
- else default_hidden_sizes(env.observation_space)
- )
- v_hidden_sizes = (
- v_hidden_sizes
- if v_hidden_sizes is not None
- else default_hidden_sizes(env.observation_space)
- )
- super().__init__(
- env,
- pi_hidden_sizes,
- v_hidden_sizes,
- **kwargs,
- )
diff --git a/ppo/ppo.py b/ppo/ppo.py
deleted file mode 100644
index b2ae5675b7d6d009ea12d7c7f2c136686f79d761..0000000000000000000000000000000000000000
--- a/ppo/ppo.py
+++ /dev/null
@@ -1,311 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from dataclasses import asdict, dataclass, field
-from torch.optim import Adam
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import List, Optional, Sequence, NamedTuple, TypeVar
-
-from shared.algorithm import Algorithm
-from shared.callbacks.callback import Callback
-from shared.gae import compute_advantage, compute_rtg_and_advantage, RtgAdvantage
-from shared.policy.on_policy import ActorCritic
-from shared.schedule import constant_schedule, linear_schedule
-from shared.trajectory import Trajectory, TrajectoryAccumulator
-
-
-@dataclass
-class PPOTrajectory(Trajectory):
- logp_a: List[float] = field(default_factory=list)
-
- def add(
- self,
- obs: np.ndarray,
- act: np.ndarray,
- next_obs: np.ndarray,
- rew: float,
- terminated: bool,
- v: float,
- logp_a: float,
- ):
- super().add(obs, act, next_obs, rew, terminated, v)
- self.logp_a.append(logp_a)
-
-
-class PPOTrajectoryAccumulator(TrajectoryAccumulator):
- def __init__(self, num_envs: int) -> None:
- super().__init__(num_envs, PPOTrajectory)
-
- def step(
- self,
- obs: VecEnvObs,
- action: np.ndarray,
- next_obs: VecEnvObs,
- reward: np.ndarray,
- done: np.ndarray,
- val: np.ndarray,
- logp_a: np.ndarray,
- ) -> None:
- super().step(obs, action, next_obs, reward, done, val, logp_a)
-
-
-class TrainStepStats(NamedTuple):
- loss: float
- pi_loss: float
- v_loss: float
- entropy_loss: float
- approx_kl: float
- clipped_frac: float
-
-
-@dataclass
-class TrainStats:
- loss: float
- pi_loss: float
- v_loss: float
- entropy_loss: float
- approx_kl: float
- clipped_frac: float
-
- def __init__(self, step_stats: List[TrainStepStats]) -> None:
- self.loss = np.mean([s.loss for s in step_stats]).item()
- self.pi_loss = np.mean([s.pi_loss for s in step_stats]).item()
- self.v_loss = np.mean([s.v_loss for s in step_stats]).item()
- self.entropy_loss = np.mean([s.entropy_loss for s in step_stats]).item()
- self.approx_kl = np.mean([s.approx_kl for s in step_stats]).item()
- self.clipped_frac = np.mean([s.clipped_frac for s in step_stats]).item()
-
- def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None:
- tb_writer.add_scalars("losses", asdict(self), global_step=global_step)
-
- def __repr__(self) -> str:
- return " | ".join(
- [
- f"Loss: {round(self.loss, 2)}",
- f"Pi L: {round(self.pi_loss, 2)}",
- f"V L: {round(self.v_loss, 2)}",
- f"E L: {round(self.entropy_loss, 2)}",
- f"Apx KL Div: {round(self.approx_kl, 2)}",
- f"Clip Frac: {round(self.clipped_frac, 2)}",
- ]
- )
-
-
-PPOSelf = TypeVar("PPOSelf", bound="PPO")
-
-
-class PPO(Algorithm):
- def __init__(
- self,
- policy: ActorCritic,
- env: VecEnv,
- device: torch.device,
- tb_writer: SummaryWriter,
- learning_rate: float = 3e-4,
- learning_rate_decay: str = "none",
- n_steps: int = 2048,
- batch_size: int = 64,
- n_epochs: int = 10,
- gamma: float = 0.99,
- gae_lambda: float = 0.95,
- clip_range: float = 0.2,
- clip_range_decay: str = "none",
- clip_range_vf: Optional[float] = None,
- clip_range_vf_decay: str = "none",
- normalize_advantage: bool = True,
- ent_coef: float = 0.0,
- ent_coef_decay: str = "none",
- vf_coef: float = 0.5,
- ppo2_vf_coef_halving: bool = False,
- max_grad_norm: float = 0.5,
- update_rtg_between_epochs: bool = False,
- sde_sample_freq: int = -1,
- ) -> None:
- super().__init__(policy, env, device, tb_writer)
- self.policy = policy
-
- self.gamma = gamma
- self.gae_lambda = gae_lambda
- self.optimizer = Adam(self.policy.parameters(), lr=learning_rate, eps=1e-7)
- self.lr_schedule = (
- linear_schedule(learning_rate, 0)
- if learning_rate_decay == "linear"
- else constant_schedule(learning_rate)
- )
- self.max_grad_norm = max_grad_norm
- self.clip_range_schedule = (
- linear_schedule(clip_range, 0)
- if clip_range_decay == "linear"
- else constant_schedule(clip_range)
- )
- self.clip_range_vf_schedule = None
- if clip_range_vf:
- self.clip_range_vf_schedule = (
- linear_schedule(clip_range_vf, 0)
- if clip_range_vf_decay == "linear"
- else constant_schedule(clip_range_vf)
- )
- self.normalize_advantage = normalize_advantage
- self.ent_coef_schedule = (
- linear_schedule(ent_coef, 0)
- if ent_coef_decay == "linear"
- else constant_schedule(ent_coef)
- )
- self.vf_coef = vf_coef
- self.ppo2_vf_coef_halving = ppo2_vf_coef_halving
-
- self.n_steps = n_steps
- self.batch_size = batch_size
- self.n_epochs = n_epochs
- self.sde_sample_freq = sde_sample_freq
-
- self.update_rtg_between_epochs = update_rtg_between_epochs
-
- def learn(
- self: PPOSelf,
- total_timesteps: int,
- callback: Optional[Callback] = None,
- ) -> PPOSelf:
- obs = self.env.reset()
- ts_elapsed = 0
- while ts_elapsed < total_timesteps:
- accumulator = self._collect_trajectories(obs)
- progress = ts_elapsed / total_timesteps
- train_stats = self.train(accumulator.all_trajectories, progress)
- rollout_steps = self.n_steps * self.env.num_envs
- ts_elapsed += rollout_steps
- train_stats.write_to_tensorboard(self.tb_writer, ts_elapsed)
- if callback:
- callback.on_step(timesteps_elapsed=rollout_steps)
-
- return self
-
- def _collect_trajectories(self, obs: VecEnvObs) -> PPOTrajectoryAccumulator:
- self.policy.eval()
- accumulator = PPOTrajectoryAccumulator(self.env.num_envs)
- self.policy.reset_noise()
- for i in range(self.n_steps):
- if self.sde_sample_freq > 0 and i > 0 and i % self.sde_sample_freq == 0:
- self.policy.reset_noise()
- action, value, logp_a, clamped_action = self.policy.step(obs)
- next_obs, reward, done, _ = self.env.step(clamped_action)
- accumulator.step(obs, action, next_obs, reward, done, value, logp_a)
- obs = next_obs
- return accumulator
-
- def train(self, trajectories: List[PPOTrajectory], progress: float) -> TrainStats:
- self.policy.train()
- learning_rate = self.lr_schedule(progress)
- self.optimizer.param_groups[0]["lr"] = learning_rate
-
- pi_clip = self.clip_range_schedule(progress)
- v_clip = (
- self.clip_range_vf_schedule(progress)
- if self.clip_range_vf_schedule
- else None
- )
- ent_coef = self.ent_coef_schedule(progress)
-
- obs = torch.as_tensor(
- np.concatenate([np.array(t.obs) for t in trajectories]), device=self.device
- )
- act = torch.as_tensor(
- np.concatenate([np.array(t.act) for t in trajectories]), device=self.device
- )
- rtg, adv = compute_rtg_and_advantage(
- trajectories, self.policy, self.gamma, self.gae_lambda, self.device
- )
- orig_v = torch.as_tensor(
- np.concatenate([np.array(t.v) for t in trajectories]), device=self.device
- )
- orig_logp_a = torch.as_tensor(
- np.concatenate([np.array(t.logp_a) for t in trajectories]),
- device=self.device,
- )
-
- step_stats = []
- for _ in range(self.n_epochs):
- if self.update_rtg_between_epochs:
- rtg, adv = compute_rtg_and_advantage(
- trajectories, self.policy, self.gamma, self.gae_lambda, self.device
- )
- else:
- adv = compute_advantage(
- trajectories, self.policy, self.gamma, self.gae_lambda, self.device
- )
- idxs = torch.randperm(len(obs))
- for i in range(0, len(obs), self.batch_size):
- mb_idxs = idxs[i : i + self.batch_size]
- mb_adv = adv[mb_idxs]
- if self.normalize_advantage:
- mb_adv = (mb_adv - mb_adv.mean(-1)) / (mb_adv.std(-1) + 1e-8)
- step_stats.append(
- self._train_step(
- pi_clip,
- v_clip,
- ent_coef,
- obs[mb_idxs],
- act[mb_idxs],
- rtg[mb_idxs],
- mb_adv,
- orig_v[mb_idxs],
- orig_logp_a[mb_idxs],
- )
- )
-
- return TrainStats(step_stats)
-
- def _train_step(
- self,
- pi_clip: float,
- v_clip: Optional[float],
- ent_coef: float,
- obs: torch.Tensor,
- act: torch.Tensor,
- rtg: torch.Tensor,
- adv: torch.Tensor,
- orig_v: torch.Tensor,
- orig_logp_a: torch.Tensor,
- ) -> TrainStepStats:
- logp_a, entropy, v = self.policy(obs, act)
- logratio = logp_a - orig_logp_a
- ratio = torch.exp(logratio)
- clip_ratio = torch.clamp(ratio, min=1 - pi_clip, max=1 + pi_clip)
- pi_loss = torch.maximum(-ratio * adv, -clip_ratio * adv).mean()
-
- v_loss_unclipped = (v - rtg) ** 2
- if v_clip:
- v_loss_clipped = (
- orig_v + torch.clamp(v - orig_v, -v_clip, v_clip) - rtg
- ) ** 2
- v_loss = torch.max(v_loss_unclipped, v_loss_clipped).mean()
- else:
- v_loss = v_loss_unclipped.mean()
- if self.ppo2_vf_coef_halving:
- v_loss *= 0.5
-
- entropy_loss = entropy.mean()
-
- loss = pi_loss - ent_coef * entropy_loss + self.vf_coef * v_loss
-
- self.optimizer.zero_grad()
- loss.backward()
- nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
- self.optimizer.step()
-
- with torch.no_grad():
- approx_kl = ((ratio - 1) - logratio).mean().cpu().numpy().item()
- clipped_frac = (
- ((ratio - 1).abs() > pi_clip).float().mean().cpu().numpy().item()
- )
- return TrainStepStats(
- loss.item(),
- pi_loss.item(),
- v_loss.item(),
- entropy_loss.item(),
- approx_kl,
- clipped_frac,
- )
diff --git a/publish/markdown_format.py b/publish/markdown_format.py
deleted file mode 100644
index 1e48fd1ef85ce19ad952b04433911b3fe11bf395..0000000000000000000000000000000000000000
--- a/publish/markdown_format.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import os
-import pandas as pd
-import wandb.apis.public
-import yaml
-
-from collections import defaultdict
-from dataclasses import dataclass, asdict
-from typing import Any, Dict, Iterable, List, NamedTuple, Optional, TypeVar
-from urllib.parse import urlparse
-
-from runner.evaluate import Evaluation
-
-EvaluationRowSelf = TypeVar("EvaluationRowSelf", bound="EvaluationRow")
-
-
-@dataclass
-class EvaluationRow:
- algo: str
- env: str
- seed: Optional[int]
- reward_mean: float
- reward_std: float
- eval_episodes: int
- best: str
- wandb_url: str
-
- @staticmethod
- def data_frame(rows: List[EvaluationRowSelf]) -> pd.DataFrame:
- results = defaultdict(list)
- for r in rows:
- for k, v in asdict(r).items():
- results[k].append(v)
- return pd.DataFrame(results)
-
-
-class EvalTableData(NamedTuple):
- run: wandb.apis.public.Run
- evaluation: Evaluation
-
-
-def evaluation_table(table_data: Iterable[EvalTableData]) -> str:
- best_stats = sorted(
- [d.evaluation.stats for d in table_data], key=lambda r: r.score, reverse=True
- )[0]
- table_data = sorted(table_data, key=lambda d: d.evaluation.config.seed() or 0)
- rows = [
- EvaluationRow(
- config.algo,
- config.env_id,
- config.seed(),
- stats.score.mean,
- stats.score.std,
- len(stats),
- "*" if stats == best_stats else "",
- f"[wandb]({r.url})",
- )
- for (r, (_, stats, config)) in table_data
- ]
- df = EvaluationRow.data_frame(rows)
- return df.to_markdown(index=False)
-
-
-def github_project_link(github_url: str) -> str:
- return f"[{urlparse(github_url).path}]({github_url})"
-
-
-def header_section(algo: str, env: str, github_url: str, wandb_report_url: str) -> str:
- algo_caps = algo.upper()
- lines = [
- f"# **{algo_caps}** Agent playing **{env}**",
- f"This is a trained model of a **{algo_caps}** agent playing **{env}** using "
- f"the {github_project_link(github_url)} repo.",
- f"All models trained at this commit can be found at {wandb_report_url}.",
- ]
- return "\n\n".join(lines)
-
-
-def github_tree_link(github_url: str, commit_hash: Optional[str]) -> str:
- if not commit_hash:
- return github_project_link(github_url)
- return f"[{commit_hash[:7]}]({github_url}/tree/{commit_hash})"
-
-
-def results_section(
- table_data: List[EvalTableData], algo: str, github_url: str, commit_hash: str
-) -> str:
- # type: ignore
- lines = [
- "## Training Results",
- f"This model was trained from {len(table_data)} trainings of **{algo.upper()}** "
- + "agents using different initial seeds. "
- + f"These agents were trained by checking out "
- + f"{github_tree_link(github_url, commit_hash)}. "
- + "The best and last models were kept from each training. "
- + "This submission has loaded the best models from each training, reevaluates "
- + "them, and selects the best model from these latest evaluations (mean - std).",
- ]
- lines.append(evaluation_table(table_data))
- return "\n\n".join(lines)
-
-
-def prerequisites_section() -> str:
- return """
-### Prerequisites: Weights & Biases (WandB)
-Training and benchmarking assumes you have a Weights & Biases project to upload runs to.
-By default training goes to a rl-algo-impls project while benchmarks go to
-rl-algo-impls-benchmarks. During training and benchmarking runs, videos of the best
-models and the model weights are uploaded to WandB.
-
-Before doing anything below, you'll need to create a wandb account and run `wandb
-login`.
-"""
-
-
-def usage_section(github_url: str, run_path: str, commit_hash: str) -> str:
- return f"""
-## Usage
-{urlparse(github_url).path}: {github_url}
-
-Note: While the model state dictionary and hyperaparameters are saved, the latest
-implementation could be sufficiently different to not be able to reproduce similar
-results. You might need to checkout the commit the agent was trained on:
-{github_tree_link(github_url, commit_hash)}.
-```
-# Downloads the model, sets hyperparameters, and runs agent for 3 episodes
-python enjoy.py --wandb-run-path={run_path}
-```
-
-Setup hasn't been completely worked out yet, so you might be best served by using Google
-Colab starting from the
-[colab_enjoy.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_enjoy.ipynb)
-notebook.
-"""
-
-
-def training_setion(
- github_url: str, commit_hash: str, algo: str, env: str, seed: Optional[int]
-) -> str:
- return f"""
-## Training
-If you want the highest chance to reproduce these results, you'll want to checkout the
-commit the agent was trained on: {github_tree_link(github_url, commit_hash)}. While
-training is deterministic, different hardware will give different results.
-
-```
-python train.py --algo {algo} --env {env} {'--seed ' + str(seed) if seed is not None else ''}
-```
-
-Setup hasn't been completely worked out yet, so you might be best served by using Google
-Colab starting from the
-[colab_train.ipynb](https://github.com/sgoodfriend/rl-algo-impls/blob/main/colab_train.ipynb)
-notebook.
-"""
-
-
-def benchmarking_section(report_url: str) -> str:
- return f"""
-## Benchmarking (with Lambda Labs instance)
-This and other models from {report_url} were generated by running a script on a Lambda
-Labs instance. In a Lambda Labs instance terminal:
-```
-git clone git@github.com:sgoodfriend/rl-algo-impls.git
-cd rl-algo-impls
-bash ./lambda_labs/setup.sh
-wandb login
-bash ./lambda_labs/benchmark.sh
-```
-
-### Alternative: Google Colab Pro+
-As an alternative,
-[colab_benchmark.ipynb](https://github.com/sgoodfriend/rl-algo-impls/tree/main/benchmarks#:~:text=colab_benchmark.ipynb),
-can be used. However, this requires a Google Colab Pro+ subscription and running across
-4 separate instances because otherwise running all jobs will exceed the 24-hour limit.
-"""
-
-
-def hyperparams_section(run_config: Dict[str, Any]) -> str:
- return f"""
-## Hyperparameters
-This isn't exactly the format of hyperparams in {os.path.join("hyperparams",
-run_config["algo"] + ".yml")}, but instead the Wandb Run Config. However, it's very
-close and has some additional data:
-```
-{yaml.dump(run_config)}
-```
-"""
-
-
-def model_card_text(
- algo: str,
- env: str,
- github_url: str,
- commit_hash: str,
- wandb_report_url: str,
- table_data: List[EvalTableData],
- best_eval: EvalTableData,
-) -> str:
- run, (_, _, config) = best_eval
- run_path = "/".join(run.path)
- return "\n\n".join(
- [
- header_section(algo, env, github_url, wandb_report_url),
- results_section(table_data, algo, github_url, commit_hash),
- prerequisites_section(),
- usage_section(github_url, run_path, commit_hash),
- training_setion(github_url, commit_hash, algo, env, config.seed()),
- benchmarking_section(wandb_report_url),
- hyperparams_section(run.config),
- ]
- )
diff --git a/replay.meta.json b/replay.meta.json
index cd14ea8af77a78fdacdc0dfcfa41f4553de525a9..038fe26454a6a142f6bed772979372623a2edbec 100644
--- a/replay.meta.json
+++ b/replay.meta.json
@@ -1 +1 @@
-{"content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\\nbuilt with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\\nconfiguration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\\nlibavutil 56. 31.100 / 56. 31.100\\nlibavcodec 58. 54.100 / 58. 54.100\\nlibavformat 58. 29.100 / 58. 29.100\\nlibavdevice 58. 8.100 / 58. 8.100\\nlibavfilter 7. 57.100 / 7. 57.100\\nlibavresample 4. 0. 0 / 4. 0. 0\\nlibswscale 5. 5.100 / 5. 5.100\\nlibswresample 3. 5.100 / 3. 5.100\\nlibpostproc 55. 5.100 / 55. 5.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "600x400", "-pix_fmt", "rgb24", "-framerate", "30", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "30", "/tmp/tmp60zfhp1t/vpg-MountainCarContinuous-v0/replay.mp4"]}, "episode": {"r": 99.03748321533203, "l": 757, "t": 4.666058}}
\ No newline at end of file
+{"content_type": "video/mp4", "encoder_version": {"backend": "ffmpeg", "version": "b'ffmpeg version 4.2.7-0ubuntu0.1 Copyright (c) 2000-2022 the FFmpeg developers\\nbuilt with gcc 9 (Ubuntu 9.4.0-1ubuntu1~20.04.1)\\nconfiguration: --prefix=/usr --extra-version=0ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared\\nlibavutil 56. 31.100 / 56. 31.100\\nlibavcodec 58. 54.100 / 58. 54.100\\nlibavformat 58. 29.100 / 58. 29.100\\nlibavdevice 58. 8.100 / 58. 8.100\\nlibavfilter 7. 57.100 / 7. 57.100\\nlibavresample 4. 0. 0 / 4. 0. 0\\nlibswscale 5. 5.100 / 5. 5.100\\nlibswresample 3. 5.100 / 3. 5.100\\nlibpostproc 55. 5.100 / 55. 5.100\\n'", "cmdline": ["ffmpeg", "-nostats", "-loglevel", "error", "-y", "-f", "rawvideo", "-s:v", "600x400", "-pix_fmt", "rgb24", "-framerate", "30", "-i", "-", "-vf", "scale=trunc(iw/2)*2:trunc(ih/2)*2", "-vcodec", "libx264", "-pix_fmt", "yuv420p", "-r", "30", "/tmp/tmp1r6b3tjy/vpg-MountainCarContinuous-v0/replay.mp4"]}, "episode": {"r": 99.03748321533203, "l": 757, "t": 4.424438}}
\ No newline at end of file
diff --git a/rl_algo_impls/benchmark_publish.py b/rl_algo_impls/benchmark_publish.py
index 1d2010efab9a48121f4b79cc7192ac6a7e5524b0..2e1045f01c316068f9192db791ae6cc10b617df6 100644
--- a/rl_algo_impls/benchmark_publish.py
+++ b/rl_algo_impls/benchmark_publish.py
@@ -54,8 +54,8 @@ def benchmark_publish() -> None:
"--virtual-display", action="store_true", help="Use headless virtual display"
)
# parser.set_defaults(
- # wandb_tags=["benchmark_e47a44c", "host_129-146-2-230"],
- # wandb_report_url="https://api.wandb.ai/links/sgoodfriend/v4wd7cp5",
+ # wandb_tags=["benchmark_2067e21", "host_155-248-199-228"],
+ # wandb_report_url="https://api.wandb.ai/links/sgoodfriend/09frjfcs",
# envs=[],
# exclude_envs=[],
# )
diff --git a/rl_algo_impls/huggingface_publish.py b/rl_algo_impls/huggingface_publish.py
index 5cf482357b0c44703a30f88b2e70a92542153781..afe1d57345bfc831044f65f331f1031310fc3dfe 100644
--- a/rl_algo_impls/huggingface_publish.py
+++ b/rl_algo_impls/huggingface_publish.py
@@ -162,6 +162,7 @@ def publish(
path_in_repo="",
commit_message=f"{algo.upper()} playing {env_id} from {github_url}/tree/{commit_hash}",
token=huggingface_token,
+ delete_patterns="*",
)
print(f"Pushed model to the hub: {repo_url}")
diff --git a/runner/config.py b/runner/config.py
deleted file mode 100644
index bd8aa836c1f545606193c9479a8522cbc4808e42..0000000000000000000000000000000000000000
--- a/runner/config.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import os
-
-from datetime import datetime
-from dataclasses import dataclass
-from typing import Any, Dict, NamedTuple, Optional, TypedDict, Union
-
-
-@dataclass
-class RunArgs:
- algo: str
- env: str
- seed: Optional[int] = None
- use_deterministic_algorithms: bool = True
-
-
-class EnvHyperparams(NamedTuple):
- is_procgen: bool = False
- n_envs: int = 1
- frame_stack: int = 1
- make_kwargs: Optional[Dict[str, Any]] = None
- no_reward_timeout_steps: Optional[int] = None
- no_reward_fire_steps: Optional[int] = None
- vec_env_class: str = "dummy"
- normalize: bool = False
- normalize_kwargs: Optional[Dict[str, Any]] = None
- rolling_length: int = 100
- train_record_video: bool = False
- video_step_interval: Union[int, float] = 1_000_000
- initial_steps_to_truncate: Optional[int] = None
-
-
-class Hyperparams(TypedDict, total=False):
- device: str
- n_timesteps: Union[int, float]
- env_hyperparams: Dict[str, Any]
- policy_hyperparams: Dict[str, Any]
- algo_hyperparams: Dict[str, Any]
- eval_params: Dict[str, Any]
-
-
-@dataclass
-class Config:
- args: RunArgs
- hyperparams: Hyperparams
- root_dir: str
- run_id: str = datetime.now().isoformat()
-
- def seed(self, training: bool = True) -> Optional[int]:
- seed = self.args.seed
- if training or seed is None:
- return seed
- return seed + self.env_hyperparams.get("n_envs", 1)
-
- @property
- def device(self) -> str:
- return self.hyperparams.get("device", "auto")
-
- @property
- def n_timesteps(self) -> int:
- return int(self.hyperparams.get("n_timesteps", 100_000))
-
- @property
- def env_hyperparams(self) -> Dict[str, Any]:
- return self.hyperparams.get("env_hyperparams", {})
-
- @property
- def policy_hyperparams(self) -> Dict[str, Any]:
- return self.hyperparams.get("policy_hyperparams", {})
-
- @property
- def algo_hyperparams(self) -> Dict[str, Any]:
- return self.hyperparams.get("algo_hyperparams", {})
-
- @property
- def eval_params(self) -> Dict[str, Any]:
- return self.hyperparams.get("eval_params", {})
-
- @property
- def algo(self) -> str:
- return self.args.algo
-
- @property
- def env_id(self) -> str:
- return self.hyperparams.get("env_id") or self.args.env
-
- def model_name(self, include_seed: bool = True) -> str:
- # Use arg env name instead of environment name
- parts = [self.algo, self.args.env]
- if include_seed and self.args.seed is not None:
- parts.append(f"S{self.args.seed}")
-
- # Assume that the custom arg name already has the necessary information
- if not self.hyperparams.get("env_id"):
- make_kwargs = self.env_hyperparams.get("make_kwargs", {})
- if make_kwargs:
- for k, v in make_kwargs.items():
- if type(v) == bool and v:
- parts.append(k)
- elif type(v) == int and v:
- parts.append(f"{k}{v}")
- else:
- parts.append(str(v))
-
- return "-".join(parts)
-
- @property
- def run_name(self) -> str:
- parts = [self.model_name(), self.run_id]
- return "-".join(parts)
-
- @property
- def saved_models_dir(self) -> str:
- return os.path.join(self.root_dir, "saved_models")
-
- @property
- def downloaded_models_dir(self) -> str:
- return os.path.join(self.root_dir, "downloaded_models")
-
- def model_dir_name(
- self,
- best: bool = False,
- extension: str = "",
- ) -> str:
- return self.model_name() + ("-best" if best else "") + extension
-
- def model_dir_path(self, best: bool = False, downloaded: bool = False) -> str:
- return os.path.join(
- self.saved_models_dir if not downloaded else self.downloaded_models_dir,
- self.model_dir_name(best=best),
- )
-
- @property
- def runs_dir(self) -> str:
- return os.path.join(self.root_dir, "runs")
-
- @property
- def tensorboard_summary_path(self) -> str:
- return os.path.join(self.runs_dir, self.run_name)
-
- @property
- def logs_path(self) -> str:
- return os.path.join(self.runs_dir, f"log.yml")
-
- @property
- def videos_dir(self) -> str:
- return os.path.join(self.root_dir, "videos")
-
- @property
- def video_prefix(self) -> str:
- return os.path.join(self.videos_dir, self.model_name())
-
- @property
- def best_videos_dir(self) -> str:
- return os.path.join(self.videos_dir, f"{self.model_name()}-best")
diff --git a/runner/env.py b/runner/env.py
deleted file mode 100644
index 99c929be2fd609a7fd9787a32e2a0cee25e77989..0000000000000000000000000000000000000000
--- a/runner/env.py
+++ /dev/null
@@ -1,256 +0,0 @@
-import gym
-import numpy as np
-import os
-
-from gym.wrappers.resize_observation import ResizeObservation
-from gym.wrappers.gray_scale_observation import GrayScaleObservation
-from gym.wrappers.frame_stack import FrameStack
-from procgen.env import ProcgenEnv
-from stable_baselines3.common.atari_wrappers import (
- MaxAndSkipEnv,
- NoopResetEnv,
-)
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv
-from stable_baselines3.common.vec_env.dummy_vec_env import DummyVecEnv
-from stable_baselines3.common.vec_env.subproc_vec_env import SubprocVecEnv
-from stable_baselines3.common.vec_env.vec_normalize import VecNormalize
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import Callable, Optional, Union
-
-from runner.config import Config, EnvHyperparams
-from shared.policy.policy import VEC_NORMALIZE_FILENAME
-from wrappers.atari_wrappers import EpisodicLifeEnv, FireOnLifeStarttEnv, ClipRewardEnv
-from wrappers.episode_record_video import EpisodeRecordVideo
-from wrappers.episode_stats_writer import EpisodeStatsWriter
-from wrappers.get_rgb_observation import GetRgbObservation
-from wrappers.initial_step_truncate_wrapper import InitialStepTruncateWrapper
-from wrappers.is_vector_env import IsVectorEnv
-from wrappers.noop_env_seed import NoopEnvSeed
-from wrappers.transpose_image_observation import TransposeImageObservation
-from wrappers.video_compat_wrapper import VideoCompatWrapper
-
-GeneralVecEnv = Union[VecEnv, gym.vector.VectorEnv, gym.Wrapper]
-
-
-def make_env(
- config: Config,
- hparams: EnvHyperparams,
- training: bool = True,
- render: bool = False,
- normalize_load_path: Optional[str] = None,
- tb_writer: Optional[SummaryWriter] = None,
-) -> GeneralVecEnv:
- if hparams.is_procgen:
- return _make_procgen_env(
- config,
- hparams,
- training=training,
- render=render,
- normalize_load_path=normalize_load_path,
- tb_writer=tb_writer,
- )
- else:
- return _make_vec_env(
- config,
- hparams,
- training=training,
- render=render,
- normalize_load_path=normalize_load_path,
- tb_writer=tb_writer,
- )
-
-
-def make_eval_env(
- config: Config,
- hparams: EnvHyperparams,
- override_n_envs: Optional[int] = None,
- **kwargs
-) -> GeneralVecEnv:
- kwargs = kwargs.copy()
- kwargs["training"] = False
- if override_n_envs is not None:
- hparams_kwargs = hparams._asdict()
- hparams_kwargs["n_envs"] = override_n_envs
- if override_n_envs == 1:
- hparams_kwargs["vec_env_class"] = "dummy"
- hparams = EnvHyperparams(**hparams_kwargs)
- return make_env(config, hparams, **kwargs)
-
-
-def _make_vec_env(
- config: Config,
- hparams: EnvHyperparams,
- training: bool = True,
- render: bool = False,
- normalize_load_path: Optional[str] = None,
- tb_writer: Optional[SummaryWriter] = None,
-) -> GeneralVecEnv:
- (
- _,
- n_envs,
- frame_stack,
- make_kwargs,
- no_reward_timeout_steps,
- no_reward_fire_steps,
- vec_env_class,
- normalize,
- normalize_kwargs,
- rolling_length,
- train_record_video,
- video_step_interval,
- initial_steps_to_truncate,
- ) = hparams
-
- if "BulletEnv" in config.env_id:
- import pybullet_envs
-
- spec = gym.spec(config.env_id)
- seed = config.seed(training=training)
-
- def make(idx: int) -> Callable[[], gym.Env]:
- env_kwargs = make_kwargs.copy() if make_kwargs is not None else {}
- if "BulletEnv" in config.env_id and render:
- env_kwargs["render"] = True
- if "CarRacing" in config.env_id:
- env_kwargs["verbose"] = 0
- if "procgen" in config.env_id:
- if not render:
- env_kwargs["render_mode"] = "rgb_array"
-
- def _make() -> gym.Env:
- env = gym.make(config.env_id, **env_kwargs)
- env = gym.wrappers.RecordEpisodeStatistics(env)
- env = VideoCompatWrapper(env)
- if training and train_record_video and idx == 0:
- env = EpisodeRecordVideo(
- env,
- config.video_prefix,
- step_increment=n_envs,
- video_step_interval=int(video_step_interval),
- )
- if training and initial_steps_to_truncate:
- env = InitialStepTruncateWrapper(
- env, idx * initial_steps_to_truncate // n_envs
- )
- if "AtariEnv" in spec.entry_point: # type: ignore
- env = NoopResetEnv(env, noop_max=30)
- env = MaxAndSkipEnv(env, skip=4)
- env = EpisodicLifeEnv(env, training=training)
- action_meanings = env.unwrapped.get_action_meanings()
- if "FIRE" in action_meanings: # type: ignore
- env = FireOnLifeStarttEnv(env, action_meanings.index("FIRE"))
- env = ClipRewardEnv(env, training=training)
- env = ResizeObservation(env, (84, 84))
- env = GrayScaleObservation(env, keep_dim=False)
- env = FrameStack(env, frame_stack)
- elif "CarRacing" in config.env_id:
- env = ResizeObservation(env, (64, 64))
- env = GrayScaleObservation(env, keep_dim=False)
- env = FrameStack(env, frame_stack)
- elif "procgen" in config.env_id:
- # env = GrayScaleObservation(env, keep_dim=False)
- env = NoopEnvSeed(env)
- env = TransposeImageObservation(env)
- if frame_stack > 1:
- env = FrameStack(env, frame_stack)
-
- if no_reward_timeout_steps:
- from wrappers.no_reward_timeout import NoRewardTimeout
-
- env = NoRewardTimeout(
- env, no_reward_timeout_steps, n_fire_steps=no_reward_fire_steps
- )
-
- if seed is not None:
- env.seed(seed + idx)
- env.action_space.seed(seed + idx)
- env.observation_space.seed(seed + idx)
-
- return env
-
- return _make
-
- VecEnvClass = {"dummy": DummyVecEnv, "subproc": SubprocVecEnv}[vec_env_class]
- venv = VecEnvClass([make(i) for i in range(n_envs)])
- if training:
- assert tb_writer
- venv = EpisodeStatsWriter(
- venv, tb_writer, training=training, rolling_length=rolling_length
- )
- if normalize:
- if normalize_load_path:
- venv = VecNormalize.load(
- os.path.join(normalize_load_path, VEC_NORMALIZE_FILENAME),
- venv, # type: ignore
- )
- else:
- venv = VecNormalize(
- venv, # type: ignore
- training=training,
- **(normalize_kwargs or {}),
- )
- if not training:
- venv.norm_reward = False
- return venv
-
-
-def _make_procgen_env(
- config: Config,
- hparams: EnvHyperparams,
- training: bool = True,
- render: bool = False,
- normalize_load_path: Optional[str] = None,
- tb_writer: Optional[SummaryWriter] = None,
-) -> GeneralVecEnv:
- (
- _,
- n_envs,
- frame_stack,
- make_kwargs,
- _, # no_reward_timeout_steps
- _, # no_reward_fire_steps
- _, # vec_env_class
- normalize,
- normalize_kwargs,
- rolling_length,
- _, # train_record_video
- _, # video_step_interval
- _, # initial_steps_to_truncate
- ) = hparams
-
- seed = config.seed(training=training)
-
- make_kwargs = make_kwargs or {}
- if not render:
- make_kwargs["render_mode"] = "rgb_array"
- if seed is not None:
- make_kwargs["rand_seed"] = seed
-
- envs = ProcgenEnv(n_envs, config.env_id, **make_kwargs)
- envs = IsVectorEnv(envs)
- envs = GetRgbObservation(envs)
- # TODO: Handle Grayscale and/or FrameStack
- envs = TransposeImageObservation(envs)
-
- envs = gym.wrappers.RecordEpisodeStatistics(envs)
-
- if seed is not None:
- envs.action_space.seed(seed)
- envs.observation_space.seed(seed)
-
- if training:
- assert tb_writer
- envs = EpisodeStatsWriter(
- envs, tb_writer, training=training, rolling_length=rolling_length
- )
- if normalize and training:
- normalize_kwargs = normalize_kwargs or {}
- # TODO: Handle reward stats saving/loading/syncing, but it's only important
- # for checkpointing
- envs = gym.wrappers.NormalizeReward(envs)
- clip_obs = normalize_kwargs.get("clip_reward", 10.0)
- envs = gym.wrappers.TransformReward(
- envs, lambda r: np.clip(r, -clip_obs, clip_obs)
- )
-
- return envs
diff --git a/runner/evaluate.py b/runner/evaluate.py
deleted file mode 100644
index 85893bacfbfdbcb7a1ba794b040d67fd6bff71b4..0000000000000000000000000000000000000000
--- a/runner/evaluate.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import shutil
-
-from dataclasses import dataclass
-from typing import NamedTuple, Optional
-
-from runner.env import make_eval_env
-from runner.config import Config, EnvHyperparams, RunArgs
-from runner.running_utils import (
- load_hyperparams,
- set_seeds,
- get_device,
- make_policy,
-)
-from shared.callbacks.eval_callback import evaluate
-from shared.policy.policy import Policy
-from shared.stats import EpisodesStats
-
-
-@dataclass
-class EvalArgs(RunArgs):
- render: bool = True
- best: bool = True
- n_envs: Optional[int] = 1
- n_episodes: int = 3
- deterministic_eval: Optional[bool] = None
- no_print_returns: bool = False
- wandb_run_path: Optional[str] = None
-
-
-class Evaluation(NamedTuple):
- policy: Policy
- stats: EpisodesStats
- config: Config
-
-
-def evaluate_model(args: EvalArgs, root_dir: str) -> Evaluation:
- if args.wandb_run_path:
- import wandb
-
- api = wandb.Api()
- run = api.run(args.wandb_run_path)
- hyperparams = run.config
-
- args.algo = hyperparams["algo"]
- args.env = hyperparams["env"]
- args.seed = hyperparams.get("seed", None)
- args.use_deterministic_algorithms = hyperparams.get(
- "use_deterministic_algorithms", True
- )
-
- config = Config(args, hyperparams, root_dir)
- model_path = config.model_dir_path(best=args.best, downloaded=True)
-
- model_archive_name = config.model_dir_name(best=args.best, extension=".zip")
- run.file(model_archive_name).download()
- if os.path.isdir(model_path):
- shutil.rmtree(model_path)
- shutil.unpack_archive(model_archive_name, model_path)
- os.remove(model_archive_name)
- else:
- hyperparams = load_hyperparams(args.algo, args.env, root_dir)
-
- config = Config(args, hyperparams, root_dir)
- model_path = config.model_dir_path(best=args.best)
-
- print(args)
-
- set_seeds(args.seed, args.use_deterministic_algorithms)
-
- env = make_eval_env(
- config,
- EnvHyperparams(**config.env_hyperparams),
- override_n_envs=args.n_envs,
- render=args.render,
- normalize_load_path=model_path,
- )
- device = get_device(config.device, env)
- policy = make_policy(
- args.algo,
- env,
- device,
- load_path=model_path,
- **config.policy_hyperparams,
- ).eval()
-
- deterministic = (
- args.deterministic_eval
- if args.deterministic_eval is not None
- else config.eval_params.get("deterministic", True)
- )
- return Evaluation(
- policy,
- evaluate(
- env,
- policy,
- args.n_episodes,
- render=args.render,
- deterministic=deterministic,
- print_returns=not args.no_print_returns,
- ),
- config,
- )
diff --git a/runner/running_utils.py b/runner/running_utils.py
deleted file mode 100644
index cae4755befa520b3be5287f5b1ec0a63c45d00d9..0000000000000000000000000000000000000000
--- a/runner/running_utils.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import argparse
-import gym
-import json
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import random
-import torch
-import torch.backends.cudnn
-import yaml
-
-from gym.spaces import Box, Discrete
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import Dict, Optional, Type, Union
-
-from runner.config import Hyperparams
-from shared.algorithm import Algorithm
-from shared.callbacks.eval_callback import EvalCallback
-from shared.policy.policy import Policy
-
-from dqn.dqn import DQN
-from dqn.policy import DQNPolicy
-from vpg.vpg import VanillaPolicyGradient
-from vpg.policy import VPGActorCritic
-from ppo.ppo import PPO
-from ppo.policy import PPOActorCritic
-
-ALGOS: Dict[str, Type[Algorithm]] = {
- "dqn": DQN,
- "vpg": VanillaPolicyGradient,
- "ppo": PPO,
-}
-POLICIES: Dict[str, Type[Policy]] = {
- "dqn": DQNPolicy,
- "vpg": VPGActorCritic,
- "ppo": PPOActorCritic,
-}
-
-HYPERPARAMS_PATH = "hyperparams"
-
-
-def base_parser(multiple: bool = True) -> argparse.ArgumentParser:
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--algo",
- default=["dqn"],
- type=str,
- choices=list(ALGOS.keys()),
- nargs="+" if multiple else 1,
- help="Abbreviation(s) of algorithm(s)",
- )
- parser.add_argument(
- "--env",
- default=["CartPole-v1"],
- type=str,
- nargs="+" if multiple else 1,
- help="Name of environment(s) in gym",
- )
- parser.add_argument(
- "--seed",
- default=[1],
- type=int,
- nargs="*" if multiple else "?",
- help="Seeds to run experiment. Unset will do one run with no set seed",
- )
- parser.add_argument(
- "--use-deterministic-algorithms",
- default=True,
- type=bool,
- help="If seed set, set torch.use_deterministic_algorithms",
- )
- return parser
-
-
-def load_hyperparams(algo: str, env_id: str, root_path: str) -> Hyperparams:
- hyperparams_path = os.path.join(root_path, HYPERPARAMS_PATH, f"{algo}.yml")
- with open(hyperparams_path, "r") as f:
- hyperparams_dict = yaml.safe_load(f)
-
- if env_id in hyperparams_dict:
- return hyperparams_dict[env_id]
-
- if "BulletEnv" in env_id:
- import pybullet_envs
- spec = gym.spec(env_id)
- if "AtariEnv" in str(spec.entry_point) and "_atari" in hyperparams_dict:
- return hyperparams_dict["_atari"]
- else:
- raise ValueError(f"{env_id} not specified in {algo} hyperparameters file")
-
-
-def get_device(device: str, env: VecEnv) -> torch.device:
- # cuda by default
- if device == "auto":
- device = "cuda"
- # Apple MPS is a second choice (sometimes)
- if device == "cuda" and not torch.cuda.is_available():
- device = "mps"
- # If no MPS, fallback to cpu
- if device == "mps" and not torch.backends.mps.is_available():
- device = "cpu"
- # Simple environments like Discreet and 1-D Boxes might also be better
- # served with the CPU.
- if device == "mps":
- obs_space = env.observation_space
- if isinstance(obs_space, Discrete):
- device = "cpu"
- elif isinstance(obs_space, Box) and len(obs_space.shape) == 1:
- device = "cpu"
- print(f"Device: {device}")
- return torch.device(device)
-
-
-def set_seeds(seed: Optional[int], use_deterministic_algorithms: bool) -> None:
- if seed is None:
- return
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.backends.cudnn.benchmark = False
- torch.use_deterministic_algorithms(use_deterministic_algorithms)
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
- # Stop warning and it would introduce stochasticity if I was using TF
- os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
-
-
-def make_policy(
- algo: str,
- env: VecEnv,
- device: torch.device,
- load_path: Optional[str] = None,
- **kwargs,
-) -> Policy:
- policy = POLICIES[algo](env, **kwargs).to(device)
- if load_path:
- policy.load(load_path)
- return policy
-
-
-def plot_eval_callback(callback: EvalCallback, tb_writer: SummaryWriter, run_name: str):
- figure = plt.figure()
- cumulative_steps = [
- (idx + 1) * callback.step_freq for idx in range(len(callback.stats))
- ]
- plt.plot(
- cumulative_steps,
- [s.score.mean for s in callback.stats],
- "b-",
- label="mean",
- )
- plt.plot(
- cumulative_steps,
- [s.score.mean - s.score.std for s in callback.stats],
- "g--",
- label="mean-std",
- )
- plt.fill_between(
- cumulative_steps,
- [s.score.min for s in callback.stats], # type: ignore
- [s.score.max for s in callback.stats], # type: ignore
- facecolor="cyan",
- label="range",
- )
- plt.xlabel("Steps")
- plt.ylabel("Score")
- plt.legend()
- plt.title(f"Eval {run_name}")
- tb_writer.add_figure("eval", figure)
-
-
-Scalar = Union[bool, str, float, int, None]
-
-
-def hparam_dict(
- hyperparams: Hyperparams, args: Dict[str, Union[Scalar, list]]
-) -> Dict[str, Scalar]:
- flattened = args.copy()
- for k, v in flattened.items():
- if isinstance(v, list):
- flattened[k] = json.dumps(v)
- for k, v in hyperparams.items():
- if isinstance(v, dict):
- for sk, sv in v.items():
- key = f"{k}/{sk}"
- if isinstance(sv, dict) or isinstance(sv, list):
- flattened[key] = str(sv)
- else:
- flattened[key] = sv
- else:
- flattened[k] = v # type: ignore
- return flattened # type: ignore
diff --git a/runner/train.py b/runner/train.py
deleted file mode 100644
index 6eda3ec63e34aa133fec7c6dac9c89e9036a2bc4..0000000000000000000000000000000000000000
--- a/runner/train.py
+++ /dev/null
@@ -1,134 +0,0 @@
-# Support for PyTorch mps mode (https://pytorch.org/docs/stable/notes/mps.html)
-import os
-
-os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
-
-import dataclasses
-import shutil
-import wandb
-import yaml
-
-from dataclasses import dataclass
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import Any, Dict, Optional, Sequence
-
-from shared.callbacks.eval_callback import EvalCallback
-from runner.config import Config, EnvHyperparams, RunArgs
-from runner.env import make_env, make_eval_env
-from runner.running_utils import (
- ALGOS,
- load_hyperparams,
- set_seeds,
- get_device,
- make_policy,
- plot_eval_callback,
- hparam_dict,
-)
-from shared.stats import EpisodesStats
-
-
-@dataclass
-class TrainArgs(RunArgs):
- wandb_project_name: Optional[str] = None
- wandb_entity: Optional[str] = None
- wandb_tags: Sequence[str] = dataclasses.field(default_factory=list)
-
-
-def train(args: TrainArgs):
- print(args)
- hyperparams = load_hyperparams(args.algo, args.env, os.getcwd())
- print(hyperparams)
- config = Config(args, hyperparams, os.getcwd())
-
- wandb_enabled = args.wandb_project_name
- if wandb_enabled:
- wandb.tensorboard.patch(
- root_logdir=config.tensorboard_summary_path, pytorch=True
- )
- wandb.init(
- project=args.wandb_project_name,
- entity=args.wandb_entity,
- config=hyperparams, # type: ignore
- name=config.run_name,
- monitor_gym=True,
- save_code=True,
- tags=args.wandb_tags,
- )
- wandb.config.update(args)
-
- tb_writer = SummaryWriter(config.tensorboard_summary_path)
-
- set_seeds(args.seed, args.use_deterministic_algorithms)
-
- env = make_env(
- config, EnvHyperparams(**config.env_hyperparams), tb_writer=tb_writer
- )
- device = get_device(config.device, env)
- policy = make_policy(args.algo, env, device, **config.policy_hyperparams)
- algo = ALGOS[args.algo](policy, env, device, tb_writer, **config.algo_hyperparams)
-
- eval_env = make_eval_env(config, EnvHyperparams(**config.env_hyperparams))
- record_best_videos = config.eval_params.get("record_best_videos", True)
- callback = EvalCallback(
- policy,
- eval_env,
- tb_writer,
- best_model_path=config.model_dir_path(best=True),
- **config.eval_params,
- video_env=make_eval_env(
- config, EnvHyperparams(**config.env_hyperparams), override_n_envs=1
- )
- if record_best_videos
- else None,
- best_video_dir=config.best_videos_dir,
- )
- algo.learn(config.n_timesteps, callback=callback)
-
- policy.save(config.model_dir_path(best=False))
-
- eval_stats = callback.evaluate(n_episodes=10, print_returns=True)
-
- plot_eval_callback(callback, tb_writer, config.run_name)
-
- log_dict: Dict[str, Any] = {
- "eval": eval_stats._asdict(),
- }
- if callback.best:
- log_dict["best_eval"] = callback.best._asdict()
- log_dict.update(hyperparams)
- log_dict.update(vars(args))
- with open(config.logs_path, "a") as f:
- yaml.dump({config.run_name: log_dict}, f)
-
- best_eval_stats: EpisodesStats = callback.best # type: ignore
- tb_writer.add_hparams(
- hparam_dict(hyperparams, vars(args)),
- {
- "hparam/best_mean": best_eval_stats.score.mean,
- "hparam/best_result": best_eval_stats.score.mean
- - best_eval_stats.score.std,
- "hparam/last_mean": eval_stats.score.mean,
- "hparam/last_result": eval_stats.score.mean - eval_stats.score.std,
- },
- None,
- config.run_name,
- )
-
- tb_writer.close()
-
- if wandb_enabled:
- wandb.run.summary["num_parameters"] = policy.num_parameters()
- wandb.run.summary[
- "num_trainable_parameters"
- ] = policy.num_trainable_parameters()
- shutil.make_archive(
- os.path.join(wandb.run.dir, config.model_dir_name()),
- "zip",
- config.model_dir_path(),
- )
- shutil.make_archive(
- os.path.join(wandb.run.dir, config.model_dir_name(best=True)),
- "zip",
- config.model_dir_path(best=True),
- )
- wandb.finish()
diff --git a/saved_models/vpg-MountainCarContinuous-v0-S3-best/vecnormalize.pkl b/saved_models/vpg-MountainCarContinuous-v0-S3-best/vecnormalize.pkl
deleted file mode 100644
index 5909ce5bf974c386f83af6576e186e40b7d506de..0000000000000000000000000000000000000000
--- a/saved_models/vpg-MountainCarContinuous-v0-S3-best/vecnormalize.pkl
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cdae91d5d329b0e30416b6c517f2c590d532fc6239ffca077a5d58986c39edd4
-size 6572
diff --git a/shared/algorithm.py b/shared/algorithm.py
deleted file mode 100644
index 925b20c3c96203c5cc47b89c379ff633138055e7..0000000000000000000000000000000000000000
--- a/shared/algorithm.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import gym
-import torch
-
-from abc import ABC, abstractmethod
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import List, Optional, TypeVar
-
-from shared.callbacks.callback import Callback
-from shared.policy.policy import Policy
-from shared.stats import EpisodesStats
-
-AlgorithmSelf = TypeVar("AlgorithmSelf", bound="Algorithm")
-
-class Algorithm(ABC):
- @abstractmethod
- def __init__(
- self,
- policy: Policy,
- env: VecEnv,
- device: torch.device,
- tb_writer: SummaryWriter,
- **kwargs,
- ) -> None:
- super().__init__()
- self.policy = policy
- self.env = env
- self.device = device
- self.tb_writer = tb_writer
-
- @abstractmethod
- def learn(
- self: AlgorithmSelf, total_timesteps: int, callback: Optional[Callback] = None
- ) -> AlgorithmSelf:
- ...
diff --git a/shared/callbacks/callback.py b/shared/callbacks/callback.py
deleted file mode 100644
index f53784b7070d680817f6b8d9fc1fd13085289b89..0000000000000000000000000000000000000000
--- a/shared/callbacks/callback.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from abc import ABC, abstractmethod
-
-
-class Callback(ABC):
-
- def __init__(self) -> None:
- super().__init__()
- self.timesteps_elapsed = 0
-
- def on_step(self, timesteps_elapsed: int = 1) -> bool:
- self.timesteps_elapsed += timesteps_elapsed
- return True
\ No newline at end of file
diff --git a/shared/callbacks/eval_callback.py b/shared/callbacks/eval_callback.py
deleted file mode 100644
index 5464b71ff038e4da6e7365ec9fb751f51a99767f..0000000000000000000000000000000000000000
--- a/shared/callbacks/eval_callback.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import itertools
-import numpy as np
-import os
-
-from copy import deepcopy
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvWrapper
-from stable_baselines3.common.vec_env.vec_normalize import VecNormalize
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import List, Optional, Union
-
-from shared.callbacks.callback import Callback
-from shared.policy.policy import Policy
-from shared.stats import Episode, EpisodeAccumulator, EpisodesStats
-from wrappers.vec_episode_recorder import VecEpisodeRecorder
-
-
-class EvaluateAccumulator(EpisodeAccumulator):
- def __init__(
- self,
- num_envs: int,
- goal_episodes: int,
- print_returns: bool = True,
- ignore_first_episode: bool = False,
- ):
- super().__init__(num_envs)
- self.completed_episodes_by_env_idx = [[] for _ in range(num_envs)]
- self.goal_episodes_per_env = int(np.ceil(goal_episodes / num_envs))
- self.print_returns = print_returns
- if ignore_first_episode:
- first_done = set()
-
- def should_record_done(idx: int) -> bool:
- has_done_first_episode = idx in first_done
- first_done.add(idx)
- return has_done_first_episode
-
- self.should_record_done = should_record_done
- else:
- self.should_record_done = lambda idx: True
-
- def on_done(self, ep_idx: int, episode: Episode) -> None:
- if (
- self.should_record_done(ep_idx)
- and len(self.completed_episodes_by_env_idx[ep_idx])
- >= self.goal_episodes_per_env
- ):
- return
- self.completed_episodes_by_env_idx[ep_idx].append(episode)
- if self.print_returns:
- print(
- f"Episode {len(self)} | "
- f"Score {episode.score} | "
- f"Length {episode.length}"
- )
-
- def __len__(self) -> int:
- return sum(len(ce) for ce in self.completed_episodes_by_env_idx)
-
- @property
- def episodes(self) -> List[Episode]:
- return list(itertools.chain(*self.completed_episodes_by_env_idx))
-
- def is_done(self) -> bool:
- return all(
- len(ce) == self.goal_episodes_per_env
- for ce in self.completed_episodes_by_env_idx
- )
-
-
-def evaluate(
- env: VecEnv,
- policy: Policy,
- n_episodes: int,
- render: bool = False,
- deterministic: bool = True,
- print_returns: bool = True,
- ignore_first_episode: bool = False,
-) -> EpisodesStats:
- policy.eval()
- episodes = EvaluateAccumulator(
- env.num_envs, n_episodes, print_returns, ignore_first_episode
- )
-
- obs = env.reset()
- while not episodes.is_done():
- act = policy.act(obs, deterministic=deterministic)
- obs, rew, done, _ = env.step(act)
- episodes.step(rew, done)
- if render:
- env.render()
- stats = EpisodesStats(episodes.episodes)
- if print_returns:
- print(stats)
- return stats
-
-
-class EvalCallback(Callback):
- def __init__(
- self,
- policy: Policy,
- env: VecEnv,
- tb_writer: SummaryWriter,
- best_model_path: Optional[str] = None,
- step_freq: Union[int, float] = 50_000,
- n_episodes: int = 10,
- save_best: bool = True,
- deterministic: bool = True,
- record_best_videos: bool = True,
- video_env: Optional[VecEnv] = None,
- best_video_dir: Optional[str] = None,
- max_video_length: int = 3600,
- ignore_first_episode: bool = False,
- ) -> None:
- super().__init__()
- self.policy = policy
- self.env = env
- self.tb_writer = tb_writer
- self.best_model_path = best_model_path
- self.step_freq = int(step_freq)
- self.n_episodes = n_episodes
- self.save_best = save_best
- self.deterministic = deterministic
- self.stats: List[EpisodesStats] = []
- self.best = None
-
- self.record_best_videos = record_best_videos
- assert video_env or not record_best_videos
- self.video_env = video_env
- assert best_video_dir or not record_best_videos
- self.best_video_dir = best_video_dir
- if best_video_dir:
- os.makedirs(best_video_dir, exist_ok=True)
- self.max_video_length = max_video_length
- self.best_video_base_path = None
-
- self.ignore_first_episode = ignore_first_episode
-
- def on_step(self, timesteps_elapsed: int = 1) -> bool:
- super().on_step(timesteps_elapsed)
- if self.timesteps_elapsed // self.step_freq >= len(self.stats):
- sync_vec_normalize(self.policy.vec_normalize, self.env)
- self.evaluate()
- return True
-
- def evaluate(
- self, n_episodes: Optional[int] = None, print_returns: Optional[bool] = None
- ) -> EpisodesStats:
- eval_stat = evaluate(
- self.env,
- self.policy,
- n_episodes or self.n_episodes,
- deterministic=self.deterministic,
- print_returns=print_returns or False,
- ignore_first_episode=self.ignore_first_episode,
- )
- self.policy.train(True)
- print(f"Eval Timesteps: {self.timesteps_elapsed} | {eval_stat}")
-
- self.stats.append(eval_stat)
-
- if not self.best or eval_stat >= self.best:
- strictly_better = not self.best or eval_stat > self.best
- self.best = eval_stat
- if self.save_best:
- assert self.best_model_path
- self.policy.save(self.best_model_path)
- print("Saved best model")
- self.best.write_to_tensorboard(
- self.tb_writer, "best_eval", self.timesteps_elapsed
- )
- if strictly_better and self.record_best_videos:
- assert self.video_env and self.best_video_dir
- sync_vec_normalize(self.policy.vec_normalize, self.video_env)
- self.best_video_base_path = os.path.join(
- self.best_video_dir, str(self.timesteps_elapsed)
- )
- video_wrapped = VecEpisodeRecorder(
- self.video_env,
- self.best_video_base_path,
- max_video_length=self.max_video_length,
- )
- video_stats = evaluate(
- video_wrapped,
- self.policy,
- 1,
- deterministic=self.deterministic,
- print_returns=False,
- )
- print(f"Saved best video: {video_stats}")
-
- eval_stat.write_to_tensorboard(self.tb_writer, "eval", self.timesteps_elapsed)
-
- return eval_stat
-
-
-def sync_vec_normalize(
- origin_vec_normalize: Optional[VecNormalize], destination_env: VecEnv
-) -> None:
- if origin_vec_normalize is not None:
- eval_env_wrapper = destination_env
- while isinstance(eval_env_wrapper, VecEnvWrapper):
- if isinstance(eval_env_wrapper, VecNormalize):
- if hasattr(origin_vec_normalize, "obs_rms"):
- eval_env_wrapper.obs_rms = deepcopy(origin_vec_normalize.obs_rms)
- eval_env_wrapper.ret_rms = deepcopy(origin_vec_normalize.ret_rms)
- eval_env_wrapper = eval_env_wrapper.venv
diff --git a/shared/gae.py b/shared/gae.py
deleted file mode 100644
index 05d87467d365696fb5cdb184aa690cce0ef745c5..0000000000000000000000000000000000000000
--- a/shared/gae.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np
-import torch
-
-from typing import NamedTuple, Sequence
-
-from shared.policy.on_policy import OnPolicy
-from shared.trajectory import Trajectory
-
-
-class RtgAdvantage(NamedTuple):
- rewards_to_go: torch.Tensor
- advantage: torch.Tensor
-
-
-def discounted_cumsum(x: np.ndarray, gamma: float) -> np.ndarray:
- dc = x.copy()
- for i in reversed(range(len(x) - 1)):
- dc[i] += gamma * dc[i + 1]
- return dc
-
-
-def compute_advantage(
- trajectories: Sequence[Trajectory],
- policy: OnPolicy,
- gamma: float,
- gae_lambda: float,
- device: torch.device,
-) -> torch.Tensor:
- advantage = []
- for traj in trajectories:
- last_val = 0
- if not traj.terminated and traj.next_obs is not None:
- last_val = policy.value(traj.next_obs)
- rew = np.append(np.array(traj.rew), last_val)
- v = np.append(np.array(traj.v), last_val)
- deltas = rew[:-1] + gamma * v[1:] - v[:-1]
- advantage.append(discounted_cumsum(deltas, gamma * gae_lambda))
- return torch.as_tensor(
- np.concatenate(advantage), dtype=torch.float32, device=device
- )
-
-
-def compute_rtg_and_advantage(
- trajectories: Sequence[Trajectory],
- policy: OnPolicy,
- gamma: float,
- gae_lambda: float,
- device: torch.device,
-) -> RtgAdvantage:
- rewards_to_go = []
- advantages = []
- for traj in trajectories:
- last_val = 0
- if not traj.terminated and traj.next_obs is not None:
- last_val = policy.value(traj.next_obs)
- rew = np.append(np.array(traj.rew), last_val)
- v = np.append(np.array(traj.v), last_val)
- deltas = rew[:-1] + gamma * v[1:] - v[:-1]
- adv = discounted_cumsum(deltas, gamma * gae_lambda)
- advantages.append(adv)
- rewards_to_go.append(v[:-1] + adv)
- return RtgAdvantage(
- torch.as_tensor(
- np.concatenate(rewards_to_go), dtype=torch.float32, device=device
- ),
- torch.as_tensor(np.concatenate(advantages), dtype=torch.float32, device=device),
- )
diff --git a/shared/module/feature_extractor.py b/shared/module/feature_extractor.py
deleted file mode 100644
index a3abeba287e9ff9da3408d0b362c6a11e878a060..0000000000000000000000000000000000000000
--- a/shared/module/feature_extractor.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import gym
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from abc import ABC, abstractmethod
-from gym.spaces import Box, Discrete
-from stable_baselines3.common.preprocessing import get_flattened_obs_dim
-from typing import Dict, Optional, Sequence, Type
-
-from shared.module.module import layer_init
-
-
-class CnnFeatureExtractor(nn.Module, ABC):
- @abstractmethod
- def __init__(
- self,
- in_channels: int,
- activation: Type[nn.Module] = nn.ReLU,
- init_layers_orthogonal: Optional[bool] = None,
- **kwargs,
- ) -> None:
- super().__init__()
-
-
-class NatureCnn(CnnFeatureExtractor):
- """
- CNN from DQN Nature paper: Mnih, Volodymyr, et al.
- "Human-level control through deep reinforcement learning."
- Nature 518.7540 (2015): 529-533.
- """
-
- def __init__(
- self,
- in_channels: int,
- activation: Type[nn.Module] = nn.ReLU,
- init_layers_orthogonal: Optional[bool] = None,
- **kwargs,
- ) -> None:
- if init_layers_orthogonal is None:
- init_layers_orthogonal = True
- super().__init__(in_channels, activation, init_layers_orthogonal)
- self.cnn = nn.Sequential(
- layer_init(
- nn.Conv2d(in_channels, 32, kernel_size=8, stride=4),
- init_layers_orthogonal,
- ),
- activation(),
- layer_init(
- nn.Conv2d(32, 64, kernel_size=4, stride=2),
- init_layers_orthogonal,
- ),
- activation(),
- layer_init(
- nn.Conv2d(64, 64, kernel_size=3, stride=1),
- init_layers_orthogonal,
- ),
- activation(),
- nn.Flatten(),
- )
-
- def forward(self, obs: torch.Tensor) -> torch.Tensor:
- return self.cnn(obs)
-
-
-class ResidualBlock(nn.Module):
- def __init__(
- self,
- channels: int,
- activation: Type[nn.Module] = nn.ReLU,
- init_layers_orthogonal: bool = False,
- ) -> None:
- super().__init__()
- self.residual = nn.Sequential(
- activation(),
- layer_init(
- nn.Conv2d(channels, channels, 3, padding=1), init_layers_orthogonal
- ),
- activation(),
- layer_init(
- nn.Conv2d(channels, channels, 3, padding=1), init_layers_orthogonal
- ),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return x + self.residual(x)
-
-
-class ConvSequence(nn.Module):
- def __init__(
- self,
- in_channels: int,
- out_channels: int,
- activation: Type[nn.Module] = nn.ReLU,
- init_layers_orthogonal: bool = False,
- ) -> None:
- super().__init__()
- self.seq = nn.Sequential(
- layer_init(
- nn.Conv2d(in_channels, out_channels, 3, padding=1),
- init_layers_orthogonal,
- ),
- nn.MaxPool2d(3, stride=2, padding=1),
- ResidualBlock(out_channels, activation, init_layers_orthogonal),
- ResidualBlock(out_channels, activation, init_layers_orthogonal),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- return self.seq(x)
-
-
-class ImpalaCnn(CnnFeatureExtractor):
- """
- IMPALA-style CNN architecture
- """
-
- def __init__(
- self,
- in_channels: int,
- activation: Type[nn.Module] = nn.ReLU,
- init_layers_orthogonal: Optional[bool] = None,
- impala_channels: Sequence[int] = (16, 32, 32),
- **kwargs,
- ) -> None:
- if init_layers_orthogonal is None:
- init_layers_orthogonal = False
- super().__init__(in_channels, activation, init_layers_orthogonal)
- sequences = []
- for out_channels in impala_channels:
- sequences.append(
- ConvSequence(
- in_channels, out_channels, activation, init_layers_orthogonal
- )
- )
- in_channels = out_channels
- sequences.extend(
- [
- activation(),
- nn.Flatten(),
- ]
- )
- self.seq = nn.Sequential(*sequences)
-
- def forward(self, obs: torch.Tensor) -> torch.Tensor:
- return self.seq(obs)
-
-
-CNN_EXTRACTORS_BY_STYLE: Dict[str, Type[CnnFeatureExtractor]] = {
- "nature": NatureCnn,
- "impala": ImpalaCnn,
-}
-
-
-class FeatureExtractor(nn.Module):
- def __init__(
- self,
- obs_space: gym.Space,
- activation: Type[nn.Module],
- init_layers_orthogonal: bool = False,
- cnn_feature_dim: int = 512,
- cnn_style: str = "nature",
- cnn_layers_init_orthogonal: Optional[bool] = None,
- impala_channels: Sequence[int] = (16, 32, 32),
- ) -> None:
- super().__init__()
- if isinstance(obs_space, Box):
- # Conv2D: (channels, height, width)
- if len(obs_space.shape) == 3:
- cnn = CNN_EXTRACTORS_BY_STYLE[cnn_style](
- obs_space.shape[0],
- activation,
- init_layers_orthogonal=cnn_layers_init_orthogonal,
- impala_channels=impala_channels,
- )
-
- def preprocess(obs: torch.Tensor) -> torch.Tensor:
- if len(obs.shape) == 3:
- obs = obs.unsqueeze(0)
- return obs.float() / 255.0
-
- with torch.no_grad():
- cnn_out = cnn(preprocess(torch.as_tensor(obs_space.sample())))
- self.preprocess = preprocess
- self.feature_extractor = nn.Sequential(
- cnn,
- layer_init(
- nn.Linear(cnn_out.shape[1], cnn_feature_dim),
- init_layers_orthogonal,
- ),
- activation(),
- )
- self.out_dim = cnn_feature_dim
- elif len(obs_space.shape) == 1:
-
- def preprocess(obs: torch.Tensor) -> torch.Tensor:
- if len(obs.shape) == 1:
- obs = obs.unsqueeze(0)
- return obs.float()
-
- self.preprocess = preprocess
- self.feature_extractor = nn.Flatten()
- self.out_dim = get_flattened_obs_dim(obs_space)
- else:
- raise ValueError(f"Unsupported observation space: {obs_space}")
- elif isinstance(obs_space, Discrete):
- self.preprocess = lambda x: F.one_hot(x, obs_space.n).float()
- self.feature_extractor = nn.Flatten()
- self.out_dim = obs_space.n
- else:
- raise NotImplementedError
-
- def forward(self, obs: torch.Tensor) -> torch.Tensor:
- if self.preprocess:
- obs = self.preprocess(obs)
- return self.feature_extractor(obs)
diff --git a/shared/module/module.py b/shared/module/module.py
deleted file mode 100644
index c579fb2a3808de47ec8d4c5233eea947b5cf0d28..0000000000000000000000000000000000000000
--- a/shared/module/module.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import numpy as np
-import torch.nn as nn
-
-from typing import Sequence, Type
-
-
-def mlp(
- layer_sizes: Sequence[int],
- activation: Type[nn.Module],
- output_activation: Type[nn.Module] = nn.Identity,
- init_layers_orthogonal: bool = False,
- final_layer_gain: float = np.sqrt(2),
-) -> nn.Module:
- layers = []
- for i in range(len(layer_sizes) - 2):
- layers.append(
- layer_init(
- nn.Linear(layer_sizes[i], layer_sizes[i + 1]), init_layers_orthogonal
- )
- )
- layers.append(activation())
- layers.append(
- layer_init(
- nn.Linear(layer_sizes[-2], layer_sizes[-1]),
- init_layers_orthogonal,
- std=final_layer_gain,
- )
- )
- layers.append(output_activation())
- return nn.Sequential(*layers)
-
-
-def layer_init(
- layer: nn.Module, init_layers_orthogonal: bool, std: float = np.sqrt(2)
-) -> nn.Module:
- if not init_layers_orthogonal:
- return layer
- nn.init.orthogonal_(layer.weight, std) # type: ignore
- nn.init.constant_(layer.bias, 0.0) # type: ignore
- return layer
diff --git a/shared/policy/actor.py b/shared/policy/actor.py
deleted file mode 100644
index 202280cb85bae1fded982c65f86520197a20ae51..0000000000000000000000000000000000000000
--- a/shared/policy/actor.py
+++ /dev/null
@@ -1,305 +0,0 @@
-import gym
-import torch
-import torch.nn as nn
-
-from abc import ABC, abstractmethod
-from gym.spaces import Box, Discrete
-from torch.distributions import Categorical, Distribution, Normal
-from typing import NamedTuple, Optional, Sequence, Type, TypeVar, Union
-
-from shared.module.feature_extractor import FeatureExtractor
-from shared.module.module import mlp
-
-
-class PiForward(NamedTuple):
- pi: Distribution
- logp_a: Optional[torch.Tensor]
- entropy: Optional[torch.Tensor]
-
-
-class Actor(nn.Module, ABC):
- @abstractmethod
- def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
- ...
-
-
-class CategoricalActorHead(Actor):
- def __init__(
- self,
- act_dim: int,
- hidden_sizes: Sequence[int] = (32,),
- activation: Type[nn.Module] = nn.Tanh,
- init_layers_orthogonal: bool = True,
- ) -> None:
- super().__init__()
- layer_sizes = tuple(hidden_sizes) + (act_dim,)
- self._fc = mlp(
- layer_sizes,
- activation,
- init_layers_orthogonal=init_layers_orthogonal,
- final_layer_gain=0.01,
- )
-
- def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
- logits = self._fc(obs)
- pi = Categorical(logits=logits)
- logp_a = None
- entropy = None
- if a is not None:
- logp_a = pi.log_prob(a)
- entropy = pi.entropy()
- return PiForward(pi, logp_a, entropy)
-
-
-class GaussianDistribution(Normal):
- def log_prob(self, a: torch.Tensor) -> torch.Tensor:
- return super().log_prob(a).sum(axis=-1)
-
- def sample(self) -> torch.Tensor:
- return self.rsample()
-
-
-class GaussianActorHead(Actor):
- def __init__(
- self,
- act_dim: int,
- hidden_sizes: Sequence[int] = (32,),
- activation: Type[nn.Module] = nn.Tanh,
- init_layers_orthogonal: bool = True,
- log_std_init: float = -0.5,
- ) -> None:
- super().__init__()
- layer_sizes = tuple(hidden_sizes) + (act_dim,)
- self.mu_net = mlp(
- layer_sizes,
- activation,
- init_layers_orthogonal=init_layers_orthogonal,
- final_layer_gain=0.01,
- )
- self.log_std = nn.Parameter(
- torch.ones(act_dim, dtype=torch.float32) * log_std_init
- )
-
- def _distribution(self, obs: torch.Tensor) -> Distribution:
- mu = self.mu_net(obs)
- std = torch.exp(self.log_std)
- return GaussianDistribution(mu, std)
-
- def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
- pi = self._distribution(obs)
- logp_a = None
- entropy = None
- if a is not None:
- logp_a = pi.log_prob(a)
- entropy = pi.entropy()
- return PiForward(pi, logp_a, entropy)
-
-
-class TanhBijector:
- def __init__(self, epsilon: float = 1e-6) -> None:
- self.epsilon = epsilon
-
- @staticmethod
- def forward(x: torch.Tensor) -> torch.Tensor:
- return torch.tanh(x)
-
- @staticmethod
- def inverse(y: torch.Tensor) -> torch.Tensor:
- eps = torch.finfo(y.dtype).eps
- clamped_y = y.clamp(min=-1.0 + eps, max=1.0 - eps)
- return torch.atanh(clamped_y)
-
- def log_prob_correction(self, x: torch.Tensor) -> torch.Tensor:
- return torch.log(1.0 - torch.tanh(x) ** 2 + self.epsilon)
-
-
-class StateDependentNoiseDistribution(Normal):
- def __init__(
- self,
- loc,
- scale,
- latent_sde: torch.Tensor,
- exploration_mat: torch.Tensor,
- exploration_matrices: torch.Tensor,
- bijector: Optional[TanhBijector] = None,
- validate_args=None,
- ):
- super().__init__(loc, scale, validate_args)
- self.latent_sde = latent_sde
- self.exploration_mat = exploration_mat
- self.exploration_matrices = exploration_matrices
- self.bijector = bijector
-
- def log_prob(self, a: torch.Tensor) -> torch.Tensor:
- gaussian_a = self.bijector.inverse(a) if self.bijector else a
- log_prob = super().log_prob(gaussian_a).sum(axis=-1)
- if self.bijector:
- log_prob -= torch.sum(self.bijector.log_prob_correction(gaussian_a), dim=1)
- return log_prob
-
- def sample(self) -> torch.Tensor:
- noise = self._get_noise()
- actions = self.mean + noise
- return self.bijector.forward(actions) if self.bijector else actions
-
- def _get_noise(self) -> torch.Tensor:
- if len(self.latent_sde) == 1 or len(self.latent_sde) != len(
- self.exploration_matrices
- ):
- return torch.mm(self.latent_sde, self.exploration_mat)
- # (batch_size, n_features) -> (batch_size, 1, n_features)
- latent_sde = self.latent_sde.unsqueeze(dim=1)
- # (batch_size, 1, n_actions)
- noise = torch.bmm(latent_sde, self.exploration_matrices)
- return noise.squeeze(dim=1)
-
- @property
- def mode(self) -> torch.Tensor:
- mean = super().mode
- return self.bijector.forward(mean) if self.bijector else mean
-
-
-StateDependentNoiseActorHeadSelf = TypeVar(
- "StateDependentNoiseActorHeadSelf", bound="StateDependentNoiseActorHead"
-)
-
-
-class StateDependentNoiseActorHead(Actor):
- def __init__(
- self,
- act_dim: int,
- hidden_sizes: Sequence[int] = (32,),
- activation: Type[nn.Module] = nn.Tanh,
- init_layers_orthogonal: bool = True,
- log_std_init: float = -0.5,
- full_std: bool = True,
- squash_output: bool = False,
- learn_std: bool = False,
- ) -> None:
- super().__init__()
- self.act_dim = act_dim
- layer_sizes = tuple(hidden_sizes) + (self.act_dim,)
- if len(layer_sizes) == 2:
- self.latent_net = nn.Identity()
- elif len(layer_sizes) > 2:
- self.latent_net = mlp(
- layer_sizes[:-1],
- activation,
- output_activation=activation,
- init_layers_orthogonal=init_layers_orthogonal,
- )
- else:
- raise ValueError("hidden_sizes must be of at least length 1")
- self.mu_net = mlp(
- layer_sizes[-2:],
- activation,
- init_layers_orthogonal=init_layers_orthogonal,
- final_layer_gain=0.01,
- )
- self.full_std = full_std
- std_dim = (hidden_sizes[-1], act_dim if self.full_std else 1)
- self.log_std = nn.Parameter(
- torch.ones(std_dim, dtype=torch.float32) * log_std_init
- )
- self.bijector = TanhBijector() if squash_output else None
- self.learn_std = learn_std
- self.device = None
-
- self.exploration_mat = None
- self.exploration_matrices = None
- self.sample_weights()
-
- def to(
- self: StateDependentNoiseActorHeadSelf,
- device: Optional[torch.device] = None,
- dtype: Optional[Union[torch.dtype, str]] = None,
- non_blocking: bool = False,
- ) -> StateDependentNoiseActorHeadSelf:
- super().to(device, dtype, non_blocking)
- self.device = device
- return self
-
- def _distribution(self, obs: torch.Tensor) -> Distribution:
- latent = self.latent_net(obs)
- mu = self.mu_net(latent)
- latent_sde = latent if self.learn_std else latent.detach()
- variance = torch.mm(latent_sde**2, self._get_std() ** 2)
- assert self.exploration_mat is not None
- assert self.exploration_matrices is not None
- return StateDependentNoiseDistribution(
- mu,
- torch.sqrt(variance + 1e-6),
- latent_sde,
- self.exploration_mat,
- self.exploration_matrices,
- self.bijector,
- )
-
- def _get_std(self) -> torch.Tensor:
- std = torch.exp(self.log_std)
- if self.full_std:
- return std
- ones = torch.ones(self.log_std.shape[0], self.act_dim)
- if self.device:
- ones = ones.to(self.device)
- return ones * std
-
- def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
- pi = self._distribution(obs)
- logp_a = None
- entropy = None
- if a is not None:
- logp_a = pi.log_prob(a)
- entropy = -logp_a
- return PiForward(pi, logp_a, entropy)
-
- def sample_weights(self, batch_size: int = 1) -> None:
- std = self._get_std()
- weights_dist = Normal(torch.zeros_like(std), std)
- # Reparametrization trick to pass gradients
- self.exploration_mat = weights_dist.rsample()
- self.exploration_matrices = weights_dist.rsample(torch.Size((batch_size,)))
-
-
-def actor_head(
- action_space: gym.Space,
- hidden_sizes: Sequence[int],
- init_layers_orthogonal: bool,
- activation: Type[nn.Module],
- log_std_init: float = -0.5,
- use_sde: bool = False,
- full_std: bool = True,
- squash_output: bool = False,
-) -> Actor:
- assert not use_sde or isinstance(
- action_space, Box
- ), "use_sde only valid if Box action_space"
- assert not squash_output or use_sde, "squash_output only valid if use_sde"
- if isinstance(action_space, Discrete):
- return CategoricalActorHead(
- action_space.n,
- hidden_sizes=hidden_sizes,
- activation=activation,
- init_layers_orthogonal=init_layers_orthogonal,
- )
- elif isinstance(action_space, Box):
- if use_sde:
- return StateDependentNoiseActorHead(
- action_space.shape[0],
- hidden_sizes=hidden_sizes,
- activation=activation,
- init_layers_orthogonal=init_layers_orthogonal,
- log_std_init=log_std_init,
- full_std=full_std,
- squash_output=squash_output,
- )
- else:
- return GaussianActorHead(
- action_space.shape[0],
- hidden_sizes=hidden_sizes,
- activation=activation,
- init_layers_orthogonal=init_layers_orthogonal,
- log_std_init=log_std_init,
- )
- else:
- raise ValueError(f"Unsupported action space: {action_space}")
diff --git a/shared/policy/critic.py b/shared/policy/critic.py
deleted file mode 100644
index 70ed0e0778c6809f943aab4da70bec3b88fc5cf2..0000000000000000000000000000000000000000
--- a/shared/policy/critic.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import gym
-import torch
-import torch.nn as nn
-
-from typing import Sequence, Type
-from shared.module.feature_extractor import FeatureExtractor
-from shared.module.module import mlp
-
-
-class CriticHead(nn.Module):
- def __init__(
- self,
- hidden_sizes: Sequence[int] = (32,),
- activation: Type[nn.Module] = nn.Tanh,
- init_layers_orthogonal: bool = True,
- ) -> None:
- super().__init__()
- layer_sizes = tuple(hidden_sizes) + (1,)
- self._fc = mlp(
- layer_sizes,
- activation,
- init_layers_orthogonal=init_layers_orthogonal,
- final_layer_gain=1.0,
- )
-
- def forward(self, obs: torch.Tensor) -> torch.Tensor:
- v = self._fc(obs)
- return v.squeeze(-1)
diff --git a/shared/policy/on_policy.py b/shared/policy/on_policy.py
deleted file mode 100644
index f18c0dfe96a2ffd17bb7bdd2368191e79f4937f0..0000000000000000000000000000000000000000
--- a/shared/policy/on_policy.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import gym
-import numpy as np
-import torch
-
-from abc import abstractmethod
-from gym.spaces import Box, Discrete, Space
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from typing import NamedTuple, Optional, Sequence, Tuple, TypeVar
-
-from shared.module.feature_extractor import FeatureExtractor
-from shared.policy.actor import PiForward, StateDependentNoiseActorHead, actor_head
-from shared.policy.critic import CriticHead
-from shared.policy.policy import ACTIVATION, Policy
-
-
-class Step(NamedTuple):
- a: np.ndarray
- v: np.ndarray
- logp_a: np.ndarray
- clamped_a: np.ndarray
-
-
-class ACForward(NamedTuple):
- logp_a: torch.Tensor
- entropy: torch.Tensor
- v: torch.Tensor
-
-
-FEAT_EXT_FILE_NAME = "feat_ext.pt"
-V_FEAT_EXT_FILE_NAME = "v_feat_ext.pt"
-PI_FILE_NAME = "pi.pt"
-V_FILE_NAME = "v.pt"
-ActorCriticSelf = TypeVar("ActorCriticSelf", bound="ActorCritic")
-
-
-def clamp_actions(
- actions: np.ndarray, action_space: gym.Space, squash_output: bool
-) -> np.ndarray:
- if isinstance(action_space, Box):
- low, high = action_space.low, action_space.high # type: ignore
- if squash_output:
- # Squashed output is already between -1 and 1. Rescale if the actual
- # output needs to something other than -1 and 1
- return low + 0.5 * (actions + 1) * (high - low)
- else:
- return np.clip(actions, low, high)
- return actions
-
-
-def default_hidden_sizes(obs_space: Space) -> Sequence[int]:
- if isinstance(obs_space, Box):
- if len(obs_space.shape) == 3:
- # By default feature extractor to output has no hidden layers
- return []
- elif len(obs_space.shape) == 1:
- return [64, 64]
- else:
- raise ValueError(f"Unsupported observation space: {obs_space}")
- elif isinstance(obs_space, Discrete):
- return [64]
- else:
- raise ValueError(f"Unsupported observation space: {obs_space}")
-
-
-class OnPolicy(Policy):
- @abstractmethod
- def value(self, obs: VecEnvObs) -> np.ndarray:
- ...
-
- @abstractmethod
- def step(self, obs: VecEnvObs) -> Step:
- ...
-
-
-class ActorCritic(OnPolicy):
- def __init__(
- self,
- env: VecEnv,
- pi_hidden_sizes: Sequence[int],
- v_hidden_sizes: Sequence[int],
- init_layers_orthogonal: bool = True,
- activation_fn: str = "tanh",
- log_std_init: float = -0.5,
- use_sde: bool = False,
- full_std: bool = True,
- squash_output: bool = False,
- share_features_extractor: bool = True,
- cnn_feature_dim: int = 512,
- cnn_style: str = "nature",
- cnn_layers_init_orthogonal: Optional[bool] = None,
- **kwargs,
- ) -> None:
- super().__init__(env, **kwargs)
- activation = ACTIVATION[activation_fn]
- observation_space = env.observation_space
- self.action_space = env.action_space
- self.squash_output = squash_output
- self.share_features_extractor = share_features_extractor
- self._feature_extractor = FeatureExtractor(
- observation_space,
- activation,
- init_layers_orthogonal=init_layers_orthogonal,
- cnn_feature_dim=cnn_feature_dim,
- cnn_style=cnn_style,
- cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
- )
- self._pi = actor_head(
- self.action_space,
- (self._feature_extractor.out_dim,) + tuple(pi_hidden_sizes),
- init_layers_orthogonal,
- activation,
- log_std_init=log_std_init,
- use_sde=use_sde,
- full_std=full_std,
- squash_output=squash_output,
- )
-
- if not share_features_extractor:
- self._v_feature_extractor = FeatureExtractor(
- observation_space,
- activation,
- init_layers_orthogonal=init_layers_orthogonal,
- cnn_feature_dim=cnn_feature_dim,
- cnn_style=cnn_style,
- cnn_layers_init_orthogonal=cnn_layers_init_orthogonal,
- )
- v_hidden_sizes = (self._v_feature_extractor.out_dim,) + tuple(
- v_hidden_sizes
- )
- else:
- self._v_feature_extractor = None
- v_hidden_sizes = (self._feature_extractor.out_dim,) + tuple(v_hidden_sizes)
- self._v = CriticHead(
- hidden_sizes=v_hidden_sizes,
- activation=activation,
- init_layers_orthogonal=init_layers_orthogonal,
- )
-
- def _pi_forward(
- self, obs: torch.Tensor, action: Optional[torch.Tensor] = None
- ) -> Tuple[PiForward, torch.Tensor]:
- p_fe = self._feature_extractor(obs)
- pi_forward = self._pi(p_fe, action)
-
- return pi_forward, p_fe
-
- def _v_forward(self, obs: torch.Tensor, p_fc: torch.Tensor) -> torch.Tensor:
- v_fe = self._v_feature_extractor(obs) if self._v_feature_extractor else p_fc
- return self._v(v_fe)
-
- def forward(self, obs: torch.Tensor, action: torch.Tensor) -> ACForward:
- (_, logp_a, entropy), p_fc = self._pi_forward(obs, action)
- v = self._v_forward(obs, p_fc)
-
- assert logp_a is not None
- assert entropy is not None
- return ACForward(logp_a, entropy, v)
-
- def value(self, obs: VecEnvObs) -> np.ndarray:
- o = self._as_tensor(obs)
- with torch.no_grad():
- fe = (
- self._v_feature_extractor(o)
- if self._v_feature_extractor
- else self._feature_extractor(o)
- )
- v = self._v(fe)
- return v.cpu().numpy()
-
- def step(self, obs: VecEnvObs) -> Step:
- o = self._as_tensor(obs)
- with torch.no_grad():
- (pi, _, _), p_fc = self._pi_forward(o)
- a = pi.sample()
- logp_a = pi.log_prob(a)
-
- v = self._v_forward(o, p_fc)
-
- a_np = a.cpu().numpy()
- clamped_a_np = clamp_actions(a_np, self.action_space, self.squash_output)
- return Step(a_np, v.cpu().numpy(), logp_a.cpu().numpy(), clamped_a_np)
-
- def act(self, obs: np.ndarray, deterministic: bool = True) -> np.ndarray:
- if not deterministic:
- return self.step(obs).clamped_a
- else:
- o = self._as_tensor(obs)
- with torch.no_grad():
- (pi, _, _), _ = self._pi_forward(o)
- a = pi.mode
- return clamp_actions(a.cpu().numpy(), self.action_space, self.squash_output)
-
- def load(self, path: str) -> None:
- super().load(path)
- self.reset_noise()
-
- def reset_noise(self, batch_size: Optional[int] = None) -> None:
- if isinstance(self._pi, StateDependentNoiseActorHead):
- self._pi.sample_weights(
- batch_size=batch_size if batch_size else self.env.num_envs
- )
diff --git a/shared/policy/policy.py b/shared/policy/policy.py
deleted file mode 100644
index 01b4259eb528629f92d9853710bcdcc5533fa09c..0000000000000000000000000000000000000000
--- a/shared/policy/policy.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import os
-import torch
-import torch.nn as nn
-
-from abc import ABC, abstractmethod
-from stable_baselines3.common.vec_env import unwrap_vec_normalize
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from typing import Dict, Optional, Type, TypeVar, Union
-
-ACTIVATION: Dict[str, Type[nn.Module]] = {
- "tanh": nn.Tanh,
- "relu": nn.ReLU,
-}
-
-VEC_NORMALIZE_FILENAME = "vecnormalize.pkl"
-MODEL_FILENAME = "model.pth"
-
-PolicySelf = TypeVar("PolicySelf", bound="Policy")
-
-
-class Policy(nn.Module, ABC):
- @abstractmethod
- def __init__(self, env: VecEnv, **kwargs) -> None:
- super().__init__()
- self.env = env
- self.vec_normalize = unwrap_vec_normalize(env)
- self.device = None
-
- def to(
- self: PolicySelf,
- device: Optional[torch.device] = None,
- dtype: Optional[Union[torch.dtype, str]] = None,
- non_blocking: bool = False,
- ) -> PolicySelf:
- super().to(device, dtype, non_blocking)
- self.device = device
- return self
-
- @abstractmethod
- def act(self, obs: VecEnvObs, deterministic: bool = True) -> np.ndarray:
- ...
-
- def save(self, path: str) -> None:
- os.makedirs(path, exist_ok=True)
-
- if self.vec_normalize:
- self.vec_normalize.save(os.path.join(path, VEC_NORMALIZE_FILENAME))
- torch.save(
- self.state_dict(),
- os.path.join(path, MODEL_FILENAME),
- )
-
- def load(self, path: str) -> None:
- # VecNormalize load occurs in env.py
- self.load_state_dict(
- torch.load(os.path.join(path, MODEL_FILENAME), map_location=self.device)
- )
-
- def reset_noise(self) -> None:
- pass
-
- def _as_tensor(self, obs: VecEnvObs) -> torch.Tensor:
- assert isinstance(obs, np.ndarray)
- o = torch.as_tensor(obs)
- if self.device is not None:
- o = o.to(self.device)
- return o
-
- def num_trainable_parameters(self) -> int:
- return sum(p.numel() for p in self.parameters() if p.requires_grad)
-
- def num_parameters(self) -> int:
- return sum(p.numel() for p in self.parameters())
diff --git a/shared/schedule.py b/shared/schedule.py
deleted file mode 100644
index 94458a32e4cdbc42d305c6d13ba8e07d9f7686c0..0000000000000000000000000000000000000000
--- a/shared/schedule.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from typing import Callable
-
-Schedule = Callable[[float], float]
-
-
-def linear_schedule(
- start_val: float, end_val: float, end_fraction: float = 1.0
-) -> Schedule:
- def func(progress_fraction: float) -> float:
- if progress_fraction >= end_fraction:
- return end_val
- else:
- return start_val + (end_val - start_val) * progress_fraction / end_fraction
-
- return func
-
-
-def constant_schedule(val: float) -> Schedule:
- return lambda f: val
diff --git a/shared/stats.py b/shared/stats.py
deleted file mode 100644
index 415cbd6db0e20b25011b255390123bb59c0e84d6..0000000000000000000000000000000000000000
--- a/shared/stats.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import numpy as np
-
-from dataclasses import dataclass
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import Dict, List, Optional, Sequence, TypeVar
-
-
-@dataclass
-class Episode:
- score: float = 0
- length: int = 0
-
-
-StatisticSelf = TypeVar("StatisticSelf", bound="Statistic")
-
-
-@dataclass
-class Statistic:
- values: np.ndarray
- round_digits: int = 2
-
- @property
- def mean(self) -> float:
- return np.mean(self.values).item()
-
- @property
- def std(self) -> float:
- return np.std(self.values).item()
-
- @property
- def min(self) -> float:
- return np.min(self.values).item()
-
- @property
- def max(self) -> float:
- return np.max(self.values).item()
-
- def sum(self) -> float:
- return np.sum(self.values).item()
-
- def __len__(self) -> int:
- return len(self.values)
-
- def _diff(self: StatisticSelf, o: StatisticSelf) -> float:
- return (self.mean - self.std) - (o.mean - o.std)
-
- def __gt__(self: StatisticSelf, o: StatisticSelf) -> bool:
- return self._diff(o) > 0
-
- def __ge__(self: StatisticSelf, o: StatisticSelf) -> bool:
- return self._diff(o) >= 0
-
- def __repr__(self) -> str:
- mean = round(self.mean, self.round_digits)
- std = round(self.std, self.round_digits)
- if self.round_digits == 0:
- mean = int(mean)
- std = int(std)
- return f"{mean} +/- {std}"
-
- def to_dict(self) -> Dict[str, float]:
- return {
- "mean": self.mean,
- "std": self.std,
- "min": self.min,
- "max": self.max,
- }
-
-
-EpisodesStatsSelf = TypeVar("EpisodesStatsSelf", bound="EpisodesStats")
-
-
-class EpisodesStats:
- episodes: Sequence[Episode]
- simple: bool
- score: Statistic
- length: Statistic
-
- def __init__(self, episodes: Sequence[Episode], simple: bool = False) -> None:
- self.episodes = episodes
- self.simple = simple
- self.score = Statistic(np.array([e.score for e in episodes]))
- self.length = Statistic(np.array([e.length for e in episodes]), round_digits=0)
-
- def __gt__(self: EpisodesStatsSelf, o: EpisodesStatsSelf) -> bool:
- return self.score > o.score
-
- def __ge__(self: EpisodesStatsSelf, o: EpisodesStatsSelf) -> bool:
- return self.score >= o.score
-
- def __repr__(self) -> str:
- return (
- f"Score: {self.score} ({round(self.score.mean - self.score.std, 2)}) | "
- f"Length: {self.length}"
- )
-
- def __len__(self) -> int:
- return len(self.episodes)
-
- def _asdict(self) -> dict:
- return {
- "n_episodes": len(self.episodes),
- "score": self.score.to_dict(),
- "length": self.length.to_dict(),
- }
-
- def write_to_tensorboard(
- self, tb_writer: SummaryWriter, main_tag: str, global_step: Optional[int] = None
- ) -> None:
- stats = {"mean": self.score.mean}
- if not self.simple:
- stats.update(
- {
- "min": self.score.min,
- "max": self.score.max,
- "result": self.score.mean - self.score.std,
- "n_episodes": len(self.episodes),
- "length": self.length.mean,
- }
- )
- tb_writer.add_scalars(
- main_tag,
- stats,
- global_step=global_step,
- )
-
-
-class EpisodeAccumulator:
- def __init__(self, num_envs: int):
- self._episodes = []
- self.current_episodes = [Episode() for _ in range(num_envs)]
-
- @property
- def episodes(self) -> List[Episode]:
- return self._episodes
-
- def step(self, reward: np.ndarray, done: np.ndarray) -> None:
- for idx, current in enumerate(self.current_episodes):
- current.score += reward[idx]
- current.length += 1
- if done[idx]:
- self._episodes.append(current)
- self.current_episodes[idx] = Episode()
- self.on_done(idx, current)
-
- def __len__(self) -> int:
- return len(self.episodes)
-
- def on_done(self, ep_idx: int, episode: Episode) -> None:
- pass
-
- def stats(self) -> EpisodesStats:
- return EpisodesStats(self.episodes)
diff --git a/shared/trajectory.py b/shared/trajectory.py
deleted file mode 100644
index fcf508e3768435e7033a4a594d778be9e2c711f0..0000000000000000000000000000000000000000
--- a/shared/trajectory.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import numpy as np
-
-from dataclasses import dataclass, field
-from stable_baselines3.common.vec_env.base_vec_env import VecEnvObs
-from typing import Generic, List, Optional, Type, TypeVar
-
-
-@dataclass
-class Trajectory:
- obs: List[np.ndarray] = field(default_factory=list)
- act: List[np.ndarray] = field(default_factory=list)
- next_obs: Optional[np.ndarray] = None
- rew: List[float] = field(default_factory=list)
- terminated: bool = False
- v: List[float] = field(default_factory=list)
-
- def add(
- self,
- obs: np.ndarray,
- act: np.ndarray,
- next_obs: np.ndarray,
- rew: float,
- terminated: bool,
- v: float,
- ):
- self.obs.append(obs)
- self.act.append(act)
- self.next_obs = next_obs if not terminated else None
- self.rew.append(rew)
- self.terminated = terminated
- self.v.append(v)
-
- def __len__(self) -> int:
- return len(self.obs)
-
-
-T = TypeVar("T", bound=Trajectory)
-
-
-class TrajectoryAccumulator(Generic[T]):
- def __init__(self, num_envs: int, trajectory_class: Type[T] = Trajectory) -> None:
- self.num_envs = num_envs
- self.trajectory_class = trajectory_class
-
- self._trajectories = []
- self._current_trajectories = [trajectory_class() for _ in range(num_envs)]
-
- def step(
- self,
- obs: VecEnvObs,
- action: np.ndarray,
- next_obs: VecEnvObs,
- reward: np.ndarray,
- done: np.ndarray,
- val: np.ndarray,
- *args,
- ) -> None:
- assert isinstance(obs, np.ndarray)
- assert isinstance(next_obs, np.ndarray)
- for i, args in enumerate(zip(obs, action, next_obs, reward, done, val, *args)):
- trajectory = self._current_trajectories[i]
- # TODO: Eventually take advantage of terminated/truncated differentiation in
- # later versions of gym.
- trajectory.add(*args)
- if done[i]:
- self._trajectories.append(trajectory)
- self._current_trajectories[i] = self.trajectory_class()
- self.on_done(i, trajectory)
-
- @property
- def all_trajectories(self) -> List[T]:
- return self._trajectories + list(
- filter(lambda t: len(t), self._current_trajectories)
- )
-
- def n_timesteps(self) -> int:
- return sum(len(t) for t in self.all_trajectories)
-
- def on_done(self, env_idx: int, trajectory: T) -> None:
- pass
diff --git a/vpg/policy.py b/vpg/policy.py
deleted file mode 100644
index 8ac8ba2e69b3d55a18214cb84dbf59bcd96a7390..0000000000000000000000000000000000000000
--- a/vpg/policy.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-from gym.spaces import Box
-from pathlib import Path
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from typing import NamedTuple, Optional, Sequence, TypeVar
-
-from shared.module.feature_extractor import FeatureExtractor
-from shared.policy.actor import (
- PiForward,
- Actor,
- StateDependentNoiseActorHead,
- actor_head,
-)
-from shared.policy.critic import CriticHead
-from shared.policy.on_policy import (
- Step,
- ACForward,
- OnPolicy,
- clamp_actions,
- default_hidden_sizes,
-)
-from shared.policy.policy import ACTIVATION
-
-PI_FILE_NAME = "pi.pt"
-V_FILE_NAME = "v.pt"
-
-
-class VPGActor(Actor):
- def __init__(self, feature_extractor: FeatureExtractor, head: Actor) -> None:
- super().__init__()
- self.feature_extractor = feature_extractor
- self.head = head
-
- def forward(self, obs: torch.Tensor, a: Optional[torch.Tensor] = None) -> PiForward:
- fe = self.feature_extractor(obs)
- return self.head(fe, a)
-
-
-class VPGActorCritic(OnPolicy):
- def __init__(
- self,
- env: VecEnv,
- hidden_sizes: Optional[Sequence[int]] = None,
- init_layers_orthogonal: bool = True,
- activation_fn: str = "tanh",
- log_std_init: float = -0.5,
- use_sde: bool = False,
- full_std: bool = True,
- squash_output: bool = False,
- **kwargs,
- ) -> None:
- super().__init__(env, **kwargs)
- activation = ACTIVATION[activation_fn]
- obs_space = env.observation_space
- self.action_space = env.action_space
- self.use_sde = use_sde
- self.squash_output = squash_output
-
- hidden_sizes = (
- hidden_sizes
- if hidden_sizes is not None
- else default_hidden_sizes(obs_space)
- )
-
- pi_feature_extractor = FeatureExtractor(
- obs_space, activation, init_layers_orthogonal=init_layers_orthogonal
- )
- pi_head = actor_head(
- self.action_space,
- (pi_feature_extractor.out_dim,) + tuple(hidden_sizes),
- init_layers_orthogonal,
- activation,
- log_std_init=log_std_init,
- use_sde=use_sde,
- full_std=full_std,
- squash_output=squash_output,
- )
- self.pi = VPGActor(pi_feature_extractor, pi_head)
-
- v_feature_extractor = FeatureExtractor(
- obs_space, activation, init_layers_orthogonal=init_layers_orthogonal
- )
- v_head = CriticHead(
- (v_feature_extractor.out_dim,) + tuple(hidden_sizes),
- activation=activation,
- init_layers_orthogonal=init_layers_orthogonal,
- )
- self.v = nn.Sequential(v_feature_extractor, v_head)
-
- def value(self, obs: VecEnvObs) -> np.ndarray:
- o = self._as_tensor(obs)
- with torch.no_grad():
- v = self.v(o)
- return v.cpu().numpy()
-
- def step(self, obs: VecEnvObs) -> Step:
- o = self._as_tensor(obs)
- with torch.no_grad():
- pi, _, _ = self.pi(o)
- a = pi.sample()
- logp_a = pi.log_prob(a)
-
- v = self.v(o)
-
- a_np = a.cpu().numpy()
- clamped_a_np = clamp_actions(a_np, self.action_space, self.squash_output)
- return Step(a_np, v.cpu().numpy(), logp_a.cpu().numpy(), clamped_a_np)
-
- def act(self, obs: np.ndarray, deterministic: bool = True) -> np.ndarray:
- if not deterministic:
- return self.step(obs).clamped_a
- else:
- o = self._as_tensor(obs)
- with torch.no_grad():
- pi, _, _ = self.pi(o)
- a = pi.mode
- return clamp_actions(a.cpu().numpy(), self.action_space, self.squash_output)
-
- def load(self, path: str) -> None:
- super().load(path)
- self.reset_noise()
-
- def reset_noise(self, batch_size: Optional[int] = None) -> None:
- if isinstance(self.pi.head, StateDependentNoiseActorHead):
- self.pi.head.sample_weights(
- batch_size=batch_size if batch_size else self.env.num_envs
- )
diff --git a/vpg/vpg.py b/vpg/vpg.py
deleted file mode 100644
index fbfacca02a1987f6c5860e4c3765000c4e743b92..0000000000000000000000000000000000000000
--- a/vpg/vpg.py
+++ /dev/null
@@ -1,158 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-
-from collections import defaultdict
-from dataclasses import dataclass, asdict
-from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvObs
-from torch.optim import Adam
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import Optional, Sequence, TypeVar
-
-from shared.algorithm import Algorithm
-from shared.callbacks.callback import Callback
-from shared.gae import compute_rtg_and_advantage, compute_advantage
-from shared.trajectory import Trajectory, TrajectoryAccumulator
-from vpg.policy import VPGActorCritic
-
-
-@dataclass
-class TrainEpochStats:
- pi_loss: float
- v_loss: float
- envs_with_done: int = 0
- episodes_done: int = 0
-
- def write_to_tensorboard(self, tb_writer: SummaryWriter, global_step: int) -> None:
- tb_writer.add_scalars("losses", asdict(self), global_step=global_step)
-
-
-class VPGTrajectoryAccumulator(TrajectoryAccumulator):
- def __init__(self, num_envs: int) -> None:
- super().__init__(num_envs, trajectory_class=Trajectory)
- self.completed_per_env: defaultdict[int, int] = defaultdict(int)
-
- def on_done(self, env_idx: int, trajectory: Trajectory) -> None:
- self.completed_per_env[env_idx] += 1
-
-
-VanillaPolicyGradientSelf = TypeVar(
- "VanillaPolicyGradientSelf", bound="VanillaPolicyGradient"
-)
-
-
-class VanillaPolicyGradient(Algorithm):
- def __init__(
- self,
- policy: VPGActorCritic,
- env: VecEnv,
- device: torch.device,
- tb_writer: SummaryWriter,
- gamma: float = 0.99,
- pi_lr: float = 3e-4,
- val_lr: float = 1e-3,
- train_v_iters: int = 80,
- gae_lambda: float = 0.97,
- max_grad_norm: float = 10.0,
- n_steps: int = 4_000,
- sde_sample_freq: int = -1,
- update_rtg_between_v_iters: bool = False,
- ) -> None:
- super().__init__(policy, env, device, tb_writer)
- self.policy = policy
-
- self.gamma = gamma
- self.gae_lambda = gae_lambda
- self.pi_optim = Adam(self.policy.pi.parameters(), lr=pi_lr)
- self.val_optim = Adam(self.policy.v.parameters(), lr=val_lr)
- self.max_grad_norm = max_grad_norm
-
- self.n_steps = n_steps
- self.train_v_iters = train_v_iters
- self.sde_sample_freq = sde_sample_freq
- self.update_rtg_between_v_iters = update_rtg_between_v_iters
-
- def learn(
- self: VanillaPolicyGradientSelf,
- total_timesteps: int,
- callback: Optional[Callback] = None,
- ) -> VanillaPolicyGradientSelf:
- timesteps_elapsed = 0
- epoch_cnt = 0
- while timesteps_elapsed < total_timesteps:
- epoch_cnt += 1
- accumulator = self._collect_trajectories()
- epoch_stats = self.train(accumulator.all_trajectories)
- epoch_stats.envs_with_done = len(accumulator.completed_per_env)
- epoch_stats.episodes_done = sum(accumulator.completed_per_env.values())
- epoch_steps = accumulator.n_timesteps()
- timesteps_elapsed += epoch_steps
- epoch_stats.write_to_tensorboard(
- self.tb_writer, global_step=timesteps_elapsed
- )
- print(
- f"Epoch: {epoch_cnt} | "
- f"Pi Loss: {round(epoch_stats.pi_loss, 2)} | "
- f"V Loss: {round(epoch_stats.v_loss, 2)} | "
- f"Total Steps: {timesteps_elapsed}"
- )
- if callback:
- callback.on_step(timesteps_elapsed=epoch_steps)
- return self
-
- def train(self, trajectories: Sequence[Trajectory]) -> TrainEpochStats:
- self.policy.train()
- obs = torch.as_tensor(
- np.concatenate([np.array(t.obs) for t in trajectories]), device=self.device
- )
- act = torch.as_tensor(
- np.concatenate([np.array(t.act) for t in trajectories]), device=self.device
- )
- rtg, adv = compute_rtg_and_advantage(
- trajectories, self.policy, self.gamma, self.gae_lambda, self.device
- )
-
- pi_loss = self._update_pi(obs, act, adv)
- v_loss = 0
- for _ in range(self.train_v_iters):
- if self.update_rtg_between_v_iters:
- rtg = compute_advantage(
- trajectories, self.policy, self.gamma, self.gae_lambda, self.device
- )
- v_loss = self._update_v(obs, rtg)
-
- return TrainEpochStats(pi_loss, v_loss)
-
- def _collect_trajectories(self) -> VPGTrajectoryAccumulator:
- self.policy.eval()
- obs = self.env.reset()
- accumulator = VPGTrajectoryAccumulator(self.env.num_envs)
- self.policy.reset_noise()
- for i in range(self.n_steps):
- if self.sde_sample_freq > 0 and i > 0 and i % self.sde_sample_freq == 0:
- self.policy.reset_noise()
- action, value, _, clamped_action = self.policy.step(obs)
- next_obs, reward, done, _ = self.env.step(clamped_action)
- accumulator.step(obs, action, next_obs, reward, done, value)
- obs = next_obs
- return accumulator
-
- def _update_pi(
- self, obs: torch.Tensor, act: torch.Tensor, adv: torch.Tensor
- ) -> float:
- self.pi_optim.zero_grad()
- _, logp, _ = self.policy.pi(obs, act)
- pi_loss = -(logp * adv).mean()
- pi_loss.backward()
- nn.utils.clip_grad_norm_(self.policy.pi.parameters(), self.max_grad_norm)
- self.pi_optim.step()
- return pi_loss.item()
-
- def _update_v(self, obs: torch.Tensor, rtg: torch.Tensor) -> float:
- self.val_optim.zero_grad()
- v = self.policy.v(obs)
- v_loss = ((v - rtg) ** 2).mean()
- v_loss.backward()
- nn.utils.clip_grad_norm_(self.policy.v.parameters(), self.max_grad_norm)
- self.val_optim.step()
- return v_loss.item()
diff --git a/wrappers/atari_wrappers.py b/wrappers/atari_wrappers.py
deleted file mode 100644
index f1091c1fb68fe3ab453ce72b76209834e3896ea5..0000000000000000000000000000000000000000
--- a/wrappers/atari_wrappers.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import gym
-import numpy as np
-
-from typing import Any, Dict, Tuple, Union
-
-ObsType = Union[np.ndarray, dict]
-ActType = Union[int, float, np.ndarray, dict]
-
-
-class EpisodicLifeEnv(gym.Wrapper):
- def __init__(self, env: gym.Env, training: bool = True, noop_act: int = 0) -> None:
- super().__init__(env)
- self.training = training
- self.noop_act = noop_act
- self.life_done_continue = False
- self.lives = 0
-
- def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]:
- obs, rew, done, info = self.env.step(action)
- new_lives = self.env.unwrapped.ale.lives()
- self.life_done_continue = new_lives != self.lives and not done
- # Only if training should life-end be marked as done
- if self.training and 0 < new_lives < self.lives:
- done = True
- self.lives = new_lives
- return obs, rew, done, info
-
- def reset(self, **kwargs) -> ObsType:
- # If life_done_continue (but not game over), then a reset should just allow the
- # game to progress to the next life.
- if self.training and self.life_done_continue:
- obs, _, _, _ = self.env.step(self.noop_act)
- else:
- obs = self.env.reset(**kwargs)
- self.lives = self.env.unwrapped.ale.lives()
- return obs
-
-
-class FireOnLifeStarttEnv(gym.Wrapper):
- def __init__(self, env: gym.Env, fire_act: int = 1) -> None:
- super().__init__(env)
- self.fire_act = fire_act
- action_meanings = env.unwrapped.get_action_meanings()
- assert action_meanings[fire_act] == "FIRE"
- assert len(action_meanings) >= 3
- self.lives = 0
- self.fire_on_next_step = True
-
- def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]:
- if self.fire_on_next_step:
- action = self.fire_act
- self.fire_on_next_step = False
- obs, rew, done, info = self.env.step(action)
- new_lives = self.env.unwrapped.ale.lives()
- if 0 < new_lives < self.lives and not done:
- self.fire_on_next_step = True
- self.lives = new_lives
- return obs, rew, done, info
-
- def reset(self, **kwargs) -> ObsType:
- self.env.reset(**kwargs)
- obs, _, done, _ = self.env.step(self.fire_act)
- if done:
- self.env.reset(**kwargs)
- obs, _, done, _ = self.env.step(2)
- if done:
- self.env.reset(**kwargs)
- self.fire_on_next_step = False
- return obs
-
-
-class ClipRewardEnv(gym.Wrapper):
- def __init__(self, env: gym.Env, training: bool = True) -> None:
- super().__init__(env)
- self.training = training
-
- def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]:
- obs, rew, done, info = self.env.step(action)
- if self.training:
- info["unclipped_reward"] = rew
- rew = np.sign(rew)
- return obs, rew, done, info
diff --git a/wrappers/episode_record_video.py b/wrappers/episode_record_video.py
deleted file mode 100644
index 7f5edd3483ae5d6c1ae29a6ce660861b0f795a30..0000000000000000000000000000000000000000
--- a/wrappers/episode_record_video.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import gym
-import numpy as np
-
-from gym.wrappers.monitoring.video_recorder import VideoRecorder
-from typing import Tuple, Union
-
-ObsType = Union[np.ndarray, dict]
-ActType = Union[int, float, np.ndarray, dict]
-
-
-class EpisodeRecordVideo(gym.Wrapper):
- def __init__(
- self,
- env: gym.Env,
- video_path_prefix: str,
- step_increment: int = 1,
- video_step_interval: int = 1_000_000,
- max_video_length: int = 3600,
- ) -> None:
- super().__init__(env)
- self.video_path_prefix = video_path_prefix
- self.step_increment = step_increment
- self.video_step_interval = video_step_interval
- self.max_video_length = max_video_length
- self.total_steps = 0
- self.next_record_video_step = 0
- self.video_recorder = None
- self.recorded_frames = 0
-
- def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
- obs, rew, done, info = self.env.step(action)
- self.total_steps += self.step_increment
- # Using first env to record episodes
- if self.video_recorder:
- self.video_recorder.capture_frame()
- self.recorded_frames += 1
- if info.get("episode"):
- episode_info = {
- k: v.item() if hasattr(v, "item") else v
- for k, v in info["episode"].items()
- }
- self.video_recorder.metadata["episode"] = episode_info
- if self.recorded_frames > self.max_video_length:
- self._close_video_recorder()
- return obs, rew, done, info
-
- def reset(self, **kwargs) -> ObsType:
- obs = self.env.reset(**kwargs)
- if self.video_recorder:
- self._close_video_recorder()
- elif self.total_steps >= self.next_record_video_step:
- self._start_video_recorder()
- return obs
-
- def _start_video_recorder(self) -> None:
- self._close_video_recorder()
-
- video_path = f"{self.video_path_prefix}-{self.next_record_video_step}"
- self.video_recorder = VideoRecorder(
- self.env,
- base_path=video_path,
- metadata={"step": self.total_steps},
- )
-
- self.video_recorder.capture_frame()
- self.recorded_frames = 1
- self.next_record_video_step += self.video_step_interval
-
- def _close_video_recorder(self) -> None:
- if self.video_recorder:
- self.video_recorder.close()
- self.video_recorder = None
- self.recorded_frames = 0
diff --git a/wrappers/episode_stats_writer.py b/wrappers/episode_stats_writer.py
deleted file mode 100644
index 9cf9fa13b1de75cf986905d3290e0e2706a8a80a..0000000000000000000000000000000000000000
--- a/wrappers/episode_stats_writer.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import gym
-import numpy as np
-
-from collections import deque
-from stable_baselines3.common.vec_env.base_vec_env import (
- VecEnvStepReturn,
- VecEnvObs,
-)
-from torch.utils.tensorboard.writer import SummaryWriter
-from typing import Any, Dict, List
-
-from shared.stats import Episode, EpisodesStats
-
-
-class EpisodeStatsWriter(gym.Wrapper):
- def __init__(
- self,
- env,
- tb_writer: SummaryWriter,
- training: bool = True,
- rolling_length=100,
- ):
- super().__init__(env)
- self.training = training
- self.tb_writer = tb_writer
- self.rolling_length = rolling_length
- self.episodes = deque(maxlen=rolling_length)
- self.total_steps = 0
- self.episode_cnt = 0
- self.last_episode_cnt_print = 0
-
- def step(self, actions: np.ndarray) -> VecEnvStepReturn:
- obs, rews, dones, infos = self.env.step(actions)
- self._record_stats(infos)
- return obs, rews, dones, infos
-
- # Support for stable_baselines3.common.vec_env.VecEnvWrapper
- def step_wait(self) -> VecEnvStepReturn:
- obs, rews, dones, infos = self.env.step_wait()
- self._record_stats(infos)
- return obs, rews, dones, infos
-
- def _record_stats(self, infos: List[Dict[str, Any]]) -> None:
- self.total_steps += getattr(self.env, "num_envs", 1)
- step_episodes = []
- for info in infos:
- ep_info = info.get("episode")
- if ep_info:
- episode = Episode(ep_info["r"], ep_info["l"])
- step_episodes.append(episode)
- self.episodes.append(episode)
- if step_episodes:
- tag = "train" if self.training else "eval"
- step_stats = EpisodesStats(step_episodes, simple=True)
- step_stats.write_to_tensorboard(self.tb_writer, tag, self.total_steps)
- rolling_stats = EpisodesStats(self.episodes)
- rolling_stats.write_to_tensorboard(
- self.tb_writer, f"{tag}_rolling", self.total_steps
- )
- self.episode_cnt += len(step_episodes)
- if self.episode_cnt >= self.last_episode_cnt_print + self.rolling_length:
- print(
- f"Episode: {self.episode_cnt} | "
- f"Steps: {self.total_steps} | "
- f"{rolling_stats}"
- )
- self.last_episode_cnt_print += self.rolling_length
-
- def reset(self) -> VecEnvObs:
- return self.env.reset()
diff --git a/wrappers/get_rgb_observation.py b/wrappers/get_rgb_observation.py
deleted file mode 100644
index 60a28f9159676ec2ff5c10011ffcc53c88f0a0f6..0000000000000000000000000000000000000000
--- a/wrappers/get_rgb_observation.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gym
-import gym.spaces.dict
-import numpy as np
-
-from gym import ObservationWrapper
-
-
-class GetRgbObservation(ObservationWrapper):
- def __init__(self, env) -> None:
- super().__init__(env)
- assert isinstance(env.observation_space, gym.spaces.dict.Dict)
-
- self.observation_space = env.observation_space["rgb"] # type: ignore
- if getattr(env, "is_vector_env"):
- self.single_observation_space = env.single_observation_space["rgb"] # type: ignore
-
- def observation(self, observation: gym.spaces.dict.Dict) -> np.ndarray:
- return observation["rgb"] # type: ignore
diff --git a/wrappers/initial_step_truncate_wrapper.py b/wrappers/initial_step_truncate_wrapper.py
deleted file mode 100644
index 6af5a58d1ef5f9007c31c9a2b9ca67f991f81f46..0000000000000000000000000000000000000000
--- a/wrappers/initial_step_truncate_wrapper.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import gym
-import numpy as np
-
-from gym.wrappers.monitoring.video_recorder import VideoRecorder
-from typing import Any, Dict, Tuple, Union
-
-ObsType = Union[np.ndarray, dict]
-ActType = Union[int, float, np.ndarray, dict]
-
-
-class InitialStepTruncateWrapper(gym.Wrapper):
- def __init__(self, env: gym.Env, initial_steps_to_truncate: int) -> None:
- super().__init__(env)
- self.initial_steps_to_truncate = initial_steps_to_truncate
- self.initialized = initial_steps_to_truncate == 0
- self.steps = 0
-
- def step(self, action: ActType) -> Tuple[ObsType, float, bool, Dict[str, Any]]:
- obs, rew, done, info = self.env.step(action)
- if not self.initialized:
- self.steps += 1
- if self.steps >= self.initial_steps_to_truncate:
- print(f"Truncation at {self.steps} steps")
- done = True
- self.initialized = True
- return obs, rew, done, info
diff --git a/wrappers/is_vector_env.py b/wrappers/is_vector_env.py
deleted file mode 100644
index e3025c1c5f051515c75e20834d63b951676cb372..0000000000000000000000000000000000000000
--- a/wrappers/is_vector_env.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import gym
-
-from typing import Any
-
-
-class IsVectorEnv(gym.Wrapper):
- """
- Override to set properties to match gym.vector.VectorEnv
- """
-
- def __init__(self, env: Any) -> None:
- super().__init__(env)
- self.is_vector_env = True
- self.single_observation_space = env.observation_space
- self.single_action_space = env.action_space
diff --git a/wrappers/no_reward_timeout.py b/wrappers/no_reward_timeout.py
deleted file mode 100644
index 9ffdb838a1240f65eaf068d0c0bab2648fdfeee7..0000000000000000000000000000000000000000
--- a/wrappers/no_reward_timeout.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import gym
-import numpy as np
-
-from typing import Optional, Tuple, Union
-
-ObsType = Union[np.ndarray, dict]
-ActType = Union[int, float, np.ndarray, dict]
-
-
-class NoRewardTimeout(gym.Wrapper):
- def __init__(
- self, env: gym.Env, n_timeout_steps: int, n_fire_steps: Optional[int] = None
- ) -> None:
- super().__init__(env)
- self.n_timeout_steps = n_timeout_steps
- self.n_fire_steps = n_fire_steps
-
- self.fire_act = None
- if n_fire_steps is not None:
- action_meanings = env.unwrapped.get_action_meanings()
- assert "FIRE" in action_meanings
- self.fire_act = action_meanings.index("FIRE")
-
- self.steps_since_reward = 0
-
- self.episode_score = 0
- self.episode_step_idx = 0
-
- def step(self, action: ActType) -> Tuple[ObsType, float, bool, dict]:
- if self.steps_since_reward == self.n_fire_steps:
- assert self.fire_act is not None
- self.print_intervention("Force fire action")
- action = self.fire_act
- obs, rew, done, info = self.env.step(action)
-
- self.episode_score += rew
- self.episode_step_idx += 1
-
- if rew != 0 or done:
- self.steps_since_reward = 0
- else:
- self.steps_since_reward += 1
- if self.steps_since_reward >= self.n_timeout_steps:
- self.print_intervention("Early terminate")
- done = True
-
- return obs, rew, done, info
-
- def reset(self, **kwargs) -> ObsType:
- self._reset_state()
- return self.env.reset(**kwargs)
-
- def _reset_state(self) -> None:
- self.steps_since_reward = 0
- self.episode_score = 0
- self.episode_step_idx = 0
-
- def print_intervention(self, tag: str) -> None:
- print(
- f"{self.__class__.__name__}: {tag} | "
- f"Score: {self.episode_score} | "
- f"Length: {self.episode_step_idx}"
- )
diff --git a/wrappers/noop_env_seed.py b/wrappers/noop_env_seed.py
deleted file mode 100644
index 473c76f48ce86056f131ab3d13017d7eed91e2f3..0000000000000000000000000000000000000000
--- a/wrappers/noop_env_seed.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import gym
-
-from typing import List, Optional
-
-
-class NoopEnvSeed(gym.Wrapper):
- """
- Wrapper to stop a seed call going to the underlying environment.
- """
-
- def seed(self, seed: Optional[int] = None) -> Optional[List[int]]:
- return None
diff --git a/wrappers/transpose_image_observation.py b/wrappers/transpose_image_observation.py
deleted file mode 100644
index 7076c9146fa28cd266a2466b58ac6a6b6555d59b..0000000000000000000000000000000000000000
--- a/wrappers/transpose_image_observation.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import gym
-import numpy as np
-
-from gym import ObservationWrapper
-from gym.spaces import Box
-
-
-class TransposeImageObservation(ObservationWrapper):
- def __init__(self, env: gym.Env) -> None:
- super().__init__(env)
-
- assert isinstance(env.observation_space, Box)
-
- obs_space = env.observation_space
- axes = tuple(i for i in range(len(obs_space.shape)))
- self._transpose_axes = axes[:-3] + (axes[-1],) + axes[-3:-1]
-
- self.observation_space = Box(
- low=np.transpose(obs_space.low, axes=self._transpose_axes),
- high=np.transpose(obs_space.high, axes=self._transpose_axes),
- shape=[obs_space.shape[idx] for idx in self._transpose_axes],
- dtype=obs_space.dtype,
- )
-
- def observation(self, obs: np.ndarray) -> np.ndarray:
- full_shape = obs.shape
- obs_shape = self.observation_space.shape
- addl_dims = len(full_shape) - len(obs_shape)
- if addl_dims > 0:
- transpose_axes = list(range(addl_dims))
- transpose_axes.extend(a + addl_dims for a in self._transpose_axes)
- else:
- transpose_axes = self._transpose_axes
- return np.transpose(obs, axes=transpose_axes)
diff --git a/wrappers/vec_episode_recorder.py b/wrappers/vec_episode_recorder.py
deleted file mode 100644
index 22607e9e3814b2e90ef231fdf5057f73cbb35302..0000000000000000000000000000000000000000
--- a/wrappers/vec_episode_recorder.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import gym
-import numpy as np
-
-from gym.wrappers.monitoring.video_recorder import VideoRecorder
-from stable_baselines3.common.vec_env.base_vec_env import (
- VecEnvStepReturn,
- VecEnvObs,
-)
-
-
-class VecEpisodeRecorder(gym.Wrapper):
- def __init__(self, env, base_path: str, max_video_length: int = 3600):
- super().__init__(env)
- self.base_path = base_path
- self.max_video_length = max_video_length
- self.video_recorder = None
- self.recorded_frames = 0
-
- def step(self, actions: np.ndarray) -> VecEnvStepReturn:
- obs, rew, dones, infos = self.env.step(actions)
- # Using first env to record episodes
- if self.video_recorder:
- self.video_recorder.capture_frame()
- self.recorded_frames += 1
- if dones[0] and infos[0].get("episode"):
- episode_info = {
- k: v.item() if hasattr(v, "item") else v
- for k, v in infos[0]["episode"].items()
- }
- self.video_recorder.metadata["episode"] = episode_info
- if dones[0] or self.recorded_frames > self.max_video_length:
- self._close_video_recorder()
- return obs, rew, dones, infos
-
- def reset(self) -> VecEnvObs:
- obs = self.env.reset()
- self._start_video_recorder()
- return obs
-
- def _start_video_recorder(self) -> None:
- self._close_video_recorder()
-
- self.video_recorder = VideoRecorder(
- self.env,
- base_path=self.base_path,
- )
-
- self.video_recorder.capture_frame()
- self.recorded_frames = 1
-
- def _close_video_recorder(self) -> None:
- if self.video_recorder:
- self.video_recorder.close()
- self.video_recorder = None
diff --git a/wrappers/vec_single_recorder.py b/wrappers/vec_single_recorder.py
deleted file mode 100644
index effe3019a43a25d3ce1b8458eb4409112a04ee2f..0000000000000000000000000000000000000000
--- a/wrappers/vec_single_recorder.py
+++ /dev/null
@@ -1,92 +0,0 @@
-import numpy as np
-
-from gym.wrappers.monitoring.video_recorder import VideoRecorder
-from stable_baselines3.common.vec_env.base_vec_env import (
- VecEnv,
- VecEnvStepReturn,
- VecEnvWrapper,
- VecEnvObs,
-)
-from typing import Optional
-
-
-class VecSingleImageWrapper(VecEnvWrapper):
- def __init__(self, venv: VecEnv):
- super().__init__(venv)
-
- def render(self, mode: str = "human") -> Optional[np.ndarray]:
- images = self.venv.get_images()
- return images[0]
-
- def step_wait(self) -> VecEnvStepReturn:
- return self.venv.step_wait()
-
- def reset(self) -> VecEnvObs:
- return self.venv.reset()
-
-
-class VecSingleRecorder(VecEnvWrapper):
- def __init__(
- self,
- venv: VecEnv,
- video_path_prefix: str,
- video_step_interval: int = 1_000_000,
- max_video_length: int = 3600,
- ):
- super().__init__(venv)
- self.single_image_wrapper = VecSingleImageWrapper(venv)
- self.video_path_prefix = video_path_prefix
- self.video_step_interval = video_step_interval
- self.max_video_length = max_video_length
- self.total_steps = 0
- self.next_record_video_step = 0
- self.video_recorder = None
- self.recorded_frames = 0
-
- def step_wait(self) -> VecEnvStepReturn:
- obs, rew, dones, infos = self.venv.step_wait()
- self.total_steps += self.venv.num_envs
- # Using first env to record episodes
- if self.video_recorder:
- self.video_recorder.capture_frame()
- self.recorded_frames += 1
- if dones[0] and infos[0].get("episode"):
- episode_info = {
- k: v.item() if hasattr(v, "item") else v
- for k, v in infos[0]["episode"].items()
- }
- self.video_recorder.metadata["episode"] = episode_info
- if dones[0] or self.recorded_frames > self.max_video_length:
- self._close_video_recorder()
- elif dones[0] and self.total_steps >= self.next_record_video_step:
- self._start_video_recorder()
- return obs, rew, dones, infos
-
- def reset(self) -> VecEnvObs:
- obs = self.venv.reset()
- if self.video_recorder:
- self._close_video_recorder()
- elif (
- not self.video_recorder and self.total_steps >= self.next_record_video_step
- ):
- self._start_video_recorder()
- return obs
-
- def _start_video_recorder(self) -> None:
- self._close_video_recorder()
-
- video_path = f"{self.video_path_prefix}-{self.next_record_video_step}"
- self.video_recorder = VideoRecorder(
- self.single_image_wrapper,
- base_path=video_path,
- metadata={"step": self.total_steps},
- )
-
- self.video_recorder.capture_frame()
- self.recorded_frames = 1
- self.next_record_video_step += self.video_step_interval
-
- def _close_video_recorder(self) -> None:
- if self.video_recorder:
- self.video_recorder.close()
- self.video_recorder = None
diff --git a/wrappers/video_compat_wrapper.py b/wrappers/video_compat_wrapper.py
deleted file mode 100644
index c75cbd43976209f4e47eceddc0b94504ed19b584..0000000000000000000000000000000000000000
--- a/wrappers/video_compat_wrapper.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import gym
-import numpy as np
-
-
-class VideoCompatWrapper(gym.Wrapper):
- def __init__(self, env: gym.Env) -> None:
- super().__init__(env)
-
- def render(self, mode="human", **kwargs):
- r = super().render(mode=mode, **kwargs)
- if mode == "rgb_array" and isinstance(r, np.ndarray) and r.dtype != np.uint8:
- r = r.astype(np.uint8)
- return r