Upload PPO LunarLander-v2 trained agent
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-CustomLunarLander.zip +3 -0
- ppo-CustomLunarLander/_stable_baselines3_version +1 -0
- ppo-CustomLunarLander/data +99 -0
- ppo-CustomLunarLander/policy.optimizer.pth +3 -0
- ppo-CustomLunarLander/policy.pth +3 -0
- ppo-CustomLunarLander/pytorch_variables.pth +3 -0
- ppo-CustomLunarLander/system_info.txt +9 -0
- replay.mp4 +0 -0
- results.json +1 -0
README.md
ADDED
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- LunarLander-v2
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: PPO
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: LunarLander-v2
|
16 |
+
type: LunarLander-v2
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: 24.17 +/- 143.75
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **PPO** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2**
|
26 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
27 |
+
|
28 |
+
## Usage (with Stable-baselines3)
|
29 |
+
TODO: Add your code
|
30 |
+
|
31 |
+
|
32 |
+
```python
|
33 |
+
from stable_baselines3 import ...
|
34 |
+
from huggingface_sb3 import load_from_hub
|
35 |
+
|
36 |
+
...
|
37 |
+
```
|
config.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7baa0e000430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7baa0e0004c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7baa0e000550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7baa0e0005e0>", "_build": "<function ActorCriticPolicy._build at 0x7baa0e000670>", "forward": "<function ActorCriticPolicy.forward at 0x7baa0e000700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7baa0e000790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7baa0e000820>", "_predict": "<function ActorCriticPolicy._predict at 0x7baa0e0008b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7baa0e000940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7baa0e0009d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7baa0e000a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7baa0dfe78c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689789575862778097, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGZb/zz1LAY/FN+QPbSLOL9uKba++EMvvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV6AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQCQyr3j+71+MAWyUS9uMAXSUR0CcjDdtEXtTdX2UKGgGR7/md7F85S3taAdLmmgIR0CcjSj7Q9iddX2UKGgGR8BWKc7yQPqcaAdLeGgIR0Ccjd/xlQMydX2UKGgGR8BVxFWn0kGBaAdLmmgIR0Ccj+jkMkQgdX2UKGgGR7/xn3Dej2zwaAdL32gIR0CckTRtgrpadX2UKGgGR0BwF7QswtaqaAdNrAFoCEdAnJOrjxTbWXV9lChoBkdAcEgOkLx7RmgHTXICaAhHQJyYQldC3PR1fZQoaAZHwCKNOGj9GZxoB0t0aAhHQJyY89mpVCJ1fZQoaAZHQDq3DaXa8HxoB0ufaAhHQJyZ1RFZxJd1fZQoaAZHwGV4OdoWYWtoB0tjaAhHQJyaY0bcXWR1fZQoaAZHwFEiWBSUC7toB0tsaAhHQJya/mdRR/F1fZQoaAZHQCR2tnwob4toB0vYaAhHQJydUYwZflZ1fZQoaAZHwFd0HbRF7UpoB0uWaAhHQJyeKJJoTPB1fZQoaAZHwF0LNCZ4Oc5oB0uPaAhHQJye8NgBtDV1fZQoaAZHP+ofuCwr1/VoB0uPaAhHQJyfwXtShrZ1fZQoaAZHwE56bMHKOktoB0u9aAhHQJygzUwztTl1fZQoaAZHwGhuDlYEGJNoB0tUaAhHQJyhS78Nx2l1fZQoaAZHwEaUFOfukUNoB0tpaAhHQJyh4b1h9b51fZQoaAZHwFUdOerdWQxoB0uFaAhHQJyinJ2dNFl1fZQoaAZHQAcJaJQ+EAZoB0vSaAhHQJyk8u01IiF1fZQoaAZHwENp5eqrBCVoB0uMaAhHQJylwUpNKyx1fZQoaAZHwEPzpLVWjoJoB0ujaAhHQJymqCbtqpN1fZQoaAZHwClTfek56t1oB0u6aAhHQJynufI0ZWJ1fZQoaAZHwD5coQWepXJoB0ukaAhHQJyoqdWhh6V1fZQoaAZHwEKx+ZPVNHpoB0uTaAhHQJypg03wTdt1fZQoaAZHwD/hPKuB+WpoB0uoaAhHQJyrgJJGvwF1fZQoaAZHwEBjqtYB/7VoB0uxaAhHQJysf7Kq4pd1fZQoaAZHQEWOgezUqhFoB0t+aAhHQJytag2606Z1fZQoaAZHQE0Q+bmU4aRoB0t7aAhHQJyuVv60pmV1fZQoaAZHwEjQs4DLbHpoB00jAWgIR0CcsH+/xlQNdX2UKGgGR0AIEXDWK/EgaAdLZGgIR0CcsUYsNDtxdX2UKGgGR8BN/KJuVHFxaAdLn2gIR0Ccs/6Ae7tidX2UKGgGR8Ar/KYAsCkoaAdLlmgIR0CctSGeMAFQdX2UKGgGR0Awdk+otL+QaAdLj2gIR0Cctlx6OYICdX2UKGgGR0BJnUkOZssQaAdLnmgIR0Cct5ufmLccdX2UKGgGR8A816CDmKZVaAdLhWgIR0CcuFlFc6eYdX2UKGgGR0AZeeYlY2bYaAdLbGgIR0CcuPT9KmKqdX2UKGgGR8BcnOqzZ6D5aAdLkmgIR0Ccuc/ub7TEdX2UKGgGR0AmOd5prULEaAdLdWgIR0CcunDsdDIBdX2UKGgGR8BiFpQSBbwCaAdLW2gIR0CcvBJ6Y3NtdX2UKGgGR8BnAlZ3cHnmaAdLTmgIR0CcvITNMXabdX2UKGgGR0BFMPn8sMAnaAdLiWgIR0CcvUR+SbH7dX2UKGgGR8BP/oLgGbCraAdLvGgIR0CcvlrBTGYKdX2UKGgGR0Au5dqtYB/7aAdLfmgIR0Ccvxqx1PnCdX2UKGgGR8A4SUTcqOLjaAdLX2gIR0Ccv8DhLoOhdX2UKGgGR8AozZIxxkupaAdLdmgIR0CcwHNxVAAydX2UKGgGR0A4jpxWDHwPaAdL3mgIR0CcwcToMa0hdX2UKGgGR8A/e4i5d4VzaAdLkGgIR0Ccw67EHdGidX2UKGgGR8BNLc0cfeUIaAdLYmgIR0CcxD4J/oaDdX2UKGgGR0AoJSR8twrEaAdLW2gIR0CcxMOAiFCcdX2UKGgGR0BxPRSNwR5DaAdNewFoCEdAnMb3q3VkMHV9lChoBkfAVsEDbJwKjWgHS5JoCEdAnMfZ35eqrHV9lChoBke/2pHVf/m1Y2gHS1hoCEdAnMhc8gZCOXV9lChoBkfASPhxJd0JW2gHS4poCEdAnMpD987ZF3V9lChoBkfAPGEOqebut2gHS+VoCEdAnMuSwB5ooXV9lChoBkdAIpgCW/rSmmgHS5doCEdAnMxtLL6k7HV9lChoBkfASRGPq9oN/mgHS3FoCEdAnM0XDJlrdnV9lChoBkdAblMdlNDc/WgHTVABaAhHQJzPAgjhUBJ1fZQoaAZHwFgqJPZZjhFoB0uSaAhHQJzQ56hQFcJ1fZQoaAZHwFTS/95yEL9oB0uOaAhHQJzRvNqxkd51fZQoaAZHwDhweyRjjJdoB0uQaAhHQJzSlIAfdRB1fZQoaAZHwEj3BFd9lVdoB0u1aAhHQJzTp2MbWEt1fZQoaAZHwCVl7tzCDVZoB0tnaAhHQJzUP9cbBGh1fZQoaAZHwEG5jMFEAo5oB0tyaAhHQJzU58VpKz11fZQoaAZHwDWyqR2bG3poB0twaAhHQJzVh9RaX8h1fZQoaAZHwCmkhLXcxj9oB0uBaAhHQJzWRwjt5Ut1fZQoaAZHQFMiUiY9gWtoB0t/aAhHQJzYEjhUBGR1fZQoaAZHwFDmCaqjrRloB0uPaAhHQJzY4tuk1uR1fZQoaAZHwEBP5Pdl/YtoB0uDaAhHQJzZoZP2wmp1fZQoaAZHwFCYSpzcRDloB0uKaAhHQJzabrjYI0J1fZQoaAZHQDSLOjZcs19oB0u2aAhHQJzbfvBrN4Z1fZQoaAZHwFCRa11GLDRoB0t0aAhHQJzcNVuJk5J1fZQoaAZHwEnRtMwlByFoB0uEaAhHQJzdEz2vjfh1fZQoaAZHwDDJ5WzWwvBoB0t3aAhHQJzdyYOUdJd1fZQoaAZHwFfl46Oo5xRoB0t7aAhHQJzf0nb7CSB1fZQoaAZHQHEiVtsN2DBoB017AWgIR0Cc4rwhW5pbdX2UKGgGR8BYwy8OCoS+aAdLdWgIR0Cc46bx3FDOdX2UKGgGR8BPd8sUZeiSaAdLfGgIR0Cc5JcVxjridX2UKGgGR8AFkUO/cnE3aAdNGAFoCEdAnOa0mY0EYHV9lChoBkfAaPcTQmeDnWgHS2NoCEdAnOkuIdlunHV9lChoBkfAPwC9EkSmImgHS5hoCEdAnOpqiwjdHnV9lChoBkdAaEYsFMZgomgHTRABaAhHQJzr/ywwCbN1fZQoaAZHQDcPacqe9SNoB0u8aAhHQJztEuQIUrV1fZQoaAZHQEUWFnIyTINoB0uPaAhHQJzt4+RoysV1fZQoaAZHwFJiFBppN9JoB0t+aAhHQJzunER8MNN1fZQoaAZHwGASpswco6VoB0tnaAhHQJzwWN4qwyJ1fZQoaAZHwDQTMMZxaPloB0uHaAhHQJzxIxk/bCd1fZQoaAZHQEdCmDUVi4JoB0uzaAhHQJzyMi7kGRp1fZQoaAZHwEV8Tot+TeRoB0uGaAhHQJzy9thuwX91fZQoaAZHwApuuA7PppxoB0t3aAhHQJzzpoi9qUN1fZQoaAZHwFAeCpWFN+NoB0uZaAhHQJz0g8lolD51fZQoaAZHQFFzxt52QnxoB0tnaAhHQJz1GK8+Ro11fZQoaAZHwEMctQKrq+toB0tiaAhHQJz1pb7j1f51fZQoaAZHwD+yVv/BFd9oB0toaAhHQJz3aI68xsV1fZQoaAZHwBfLfpD/lyRoB0uEaAhHQJz4J/gBLf11fZQoaAZHwCz0E9t/FzdoB0vKaAhHQJz5UDbJwKl1fZQoaAZHQDREtkFwDNhoB0u7aAhHQJz6ZNlAeJZ1fZQoaAZHwEqXDTjNpudoB0t3aAhHQJz7F82Jiy91fZQoaAZHQFHhyAxzq8loB0udaAhHQJz8CqzZ6D51fZQoaAZHwFDsrlvIfbNoB0traAhHQJz8qZYxL011ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-CustomLunarLander.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:24d992b0763326160686cf4e0b3171807a67eb967a0dd67d40429e4537974dd5
|
3 |
+
size 145975
|
ppo-CustomLunarLander/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.0.0a5
|
ppo-CustomLunarLander/data
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
+
"__module__": "stable_baselines3.common.policies",
|
6 |
+
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x7baa0e000430>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7baa0e0004c0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7baa0e000550>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7baa0e0005e0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7baa0e000670>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7baa0e000700>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7baa0e000790>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7baa0e000820>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7baa0e0008b0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7baa0e000940>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7baa0e0009d0>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7baa0e000a60>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7baa0dfe78c0>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 1000448,
|
25 |
+
"_total_timesteps": 1000000,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": null,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1689789575862778097,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAGZb/zz1LAY/FN+QPbSLOL9uKba++EMvvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
|
35 |
+
},
|
36 |
+
"_last_episode_starts": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
39 |
+
},
|
40 |
+
"_last_original_obs": null,
|
41 |
+
"_episode_num": 0,
|
42 |
+
"use_sde": false,
|
43 |
+
"sde_sample_freq": -1,
|
44 |
+
"_current_progress_remaining": -0.00044800000000000395,
|
45 |
+
"_stats_window_size": 100,
|
46 |
+
"ep_info_buffer": {
|
47 |
+
":type:": "<class 'collections.deque'>",
|
48 |
+
":serialized:": "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"
|
49 |
+
},
|
50 |
+
"ep_success_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
+
},
|
54 |
+
"_n_updates": 3908,
|
55 |
+
"observation_space": {
|
56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
+
":serialized:": "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",
|
58 |
+
"dtype": "float32",
|
59 |
+
"bounded_below": "[ True True True True True True True True]",
|
60 |
+
"bounded_above": "[ True True True True True True True True]",
|
61 |
+
"_shape": [
|
62 |
+
8
|
63 |
+
],
|
64 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
65 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
66 |
+
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
67 |
+
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
68 |
+
"_np_random": null
|
69 |
+
},
|
70 |
+
"action_space": {
|
71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
+
":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
|
73 |
+
"n": "4",
|
74 |
+
"start": "0",
|
75 |
+
"_shape": [],
|
76 |
+
"dtype": "int64",
|
77 |
+
"_np_random": null
|
78 |
+
},
|
79 |
+
"n_envs": 1,
|
80 |
+
"n_steps": 1024,
|
81 |
+
"gamma": 0.999,
|
82 |
+
"gae_lambda": 0.98,
|
83 |
+
"ent_coef": 0.01,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 64,
|
87 |
+
"n_epochs": 4,
|
88 |
+
"clip_range": {
|
89 |
+
":type:": "<class 'function'>",
|
90 |
+
":serialized:": "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"
|
91 |
+
},
|
92 |
+
"clip_range_vf": null,
|
93 |
+
"normalize_advantage": true,
|
94 |
+
"target_kl": null,
|
95 |
+
"lr_schedule": {
|
96 |
+
":type:": "<class 'function'>",
|
97 |
+
":serialized:": "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"
|
98 |
+
}
|
99 |
+
}
|
ppo-CustomLunarLander/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:24cca4ba08e79aad3f22e034f914c209933c36ca6c8ae2a978c2ef9bd71f7ee7
|
3 |
+
size 87929
|
ppo-CustomLunarLander/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:21e2a03f90baf5fdbfe39b65f863b7d9bcde87f5681ddbac6d4cf62ddc98cbe2
|
3 |
+
size 43329
|
ppo-CustomLunarLander/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
3 |
+
size 431
|
ppo-CustomLunarLander/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
|
2 |
+
- Python: 3.10.6
|
3 |
+
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.0.1+cu118
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (200 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 24.170770104558024, "std_reward": 143.75144948086228, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-19T18:35:49.792963"}
|