rl_algo_impls/runner/running_utils.py

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 dataclasses import asdict
from gym.spaces import Box, Discrete
from pathlib import Path
from torch.utils.tensorboard.writer import SummaryWriter
from typing import Dict, Optional, Type, Union

from rl_algo_impls.runner.config import Hyperparams
from rl_algo_impls.shared.algorithm import Algorithm
from rl_algo_impls.shared.callbacks.eval_callback import EvalCallback
from rl_algo_impls.shared.policy.on_policy import ActorCritic
from rl_algo_impls.shared.policy.policy import Policy

from rl_algo_impls.a2c.a2c import A2C
from rl_algo_impls.dqn.dqn import DQN
from rl_algo_impls.dqn.policy import DQNPolicy
from rl_algo_impls.ppo.ppo import PPO
from rl_algo_impls.vpg.vpg import VanillaPolicyGradient
from rl_algo_impls.vpg.policy import VPGActorCritic
from rl_algo_impls.wrappers.vectorable_wrapper import VecEnv, single_observation_space

ALGOS: Dict[str, Type[Algorithm]] = {
    "dqn": DQN,
    "vpg": VanillaPolicyGradient,
    "ppo": PPO,
    "a2c": A2C,
}
POLICIES: Dict[str, Type[Policy]] = {
    "dqn": DQNPolicy,
    "vpg": VPGActorCritic,
    "ppo": ActorCritic,
    "a2c": ActorCritic,
}

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",
    )
    return parser


def load_hyperparams(algo: str, env_id: str) -> Hyperparams:
    root_path = Path(__file__).parent.parent
    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(**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(**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 = single_observation_space(env)
        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 asdict(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