Initial commit

.gitattributes

@@ -25,3 +25,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+vec_normalize.pkl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,66 @@
+---
+library_name: stable-baselines3
+tags:
+- BipedalWalkerHardcore-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 48.61 +/- 120.68
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BipedalWalkerHardcore-v3
+      type: BipedalWalkerHardcore-v3
+---
+
+# **PPO** Agent playing **BipedalWalkerHardcore-v3**
+This is a trained model of a **PPO** agent playing **BipedalWalkerHardcore-v3**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
+
+The RL Zoo is a training framework for Stable Baselines3
+reinforcement learning agents,
+with hyperparameter optimization and pre-trained agents included.
+
+## Usage (with SB3 RL Zoo)
+
+RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
+SB3: https://github.com/DLR-RM/stable-baselines3<br/>
+SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
+
+```
+# Download model and save it into the logs/ folder
+python -m utils.load_from_hub --algo ppo --env BipedalWalkerHardcore-v3 -orga sb3 -f logs/
+python enjoy.py --algo ppo --env BipedalWalkerHardcore-v3  -f logs/
+```
+
+## Training (with the RL Zoo)
+```
+python train.py --algo ppo --env BipedalWalkerHardcore-v3 -f logs/
+# Upload the model and generate video (when possible)
+python -m utils.push_to_hub --algo ppo --env BipedalWalkerHardcore-v3 -f logs/ -orga sb3
+```
+
+## Hyperparameters
+```python
+OrderedDict([('batch_size', 64),
+             ('clip_range', 'lin_0.2'),
+             ('ent_coef', 0.001),
+             ('gae_lambda', 0.95),
+             ('gamma', 0.99),
+             ('learning_rate', 'lin_2.5e-4'),
+             ('n_envs', 16),
+             ('n_epochs', 10),
+             ('n_steps', 2048),
+             ('n_timesteps', 100000000.0),
+             ('normalize', True),
+             ('policy', 'MlpPolicy'),
+             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
+```

args.yml

@@ -0,0 +1,59 @@
+!!python/object/apply:collections.OrderedDict
+- - - algo
+    - ppo
+  - - env
+    - BipedalWalkerHardcore-v3
+  - - env_kwargs
+    - null
+  - - eval_episodes
+    - 10
+  - - eval_freq
+    - 50000
+  - - gym_packages
+    - []
+  - - hyperparams
+    - null
+  - - log_folder
+    - rl-trained-agents/
+  - - log_interval
+    - -1
+  - - n_evaluations
+    - 20
+  - - n_jobs
+    - 1
+  - - n_startup_trials
+    - 10
+  - - n_timesteps
+    - -1
+  - - n_trials
+    - 10
+  - - num_threads
+    - -1
+  - - optimize_hyperparameters
+    - false
+  - - pruner
+    - median
+  - - sampler
+    - tpe
+  - - save_freq
+    - -1
+  - - save_replay_buffer
+    - false
+  - - seed
+    - 136751696
+  - - storage
+    - null
+  - - study_name
+    - null
+  - - tensorboard_log
+    - ''
+  - - trained_agent
+    - ''
+  - - truncate_last_trajectory
+    - true
+  - - uuid
+    - false
+  - - vec_env
+    - dummy
+  - - verbose
+    - 1

config.yml

@@ -0,0 +1,25 @@
+!!python/object/apply:collections.OrderedDict
+- - - batch_size
+    - 64
+  - - clip_range
+    - lin_0.2
+  - - ent_coef
+    - 0.001
+  - - gae_lambda
+    - 0.95
+  - - gamma
+    - 0.99
+  - - learning_rate
+    - lin_2.5e-4
+  - - n_envs
+    - 16
+  - - n_epochs
+    - 10
+  - - n_steps
+    - 2048
+  - - n_timesteps
+    - 100000000.0
+  - - normalize
+    - true
+  - - policy
+    - MlpPolicy

env_kwargs.yml

@@ -0,0 +1 @@
+{}

ppo-BipedalWalkerHardcore-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0fa74b5ebaf0647a241335614b172e21be950b5c1624c38884d7f45c34c1d163
+size 177749

ppo-BipedalWalkerHardcore-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.1a8

ppo-BipedalWalkerHardcore-v3/data

@@ -0,0 +1,103 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7fd4ae85c950>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd4ae85c9e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd4ae85ca70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd4ae85cb00>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd4ae85cb90>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd4ae85cc20>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd4ae85ccb0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd4ae85cd40>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd4ae85cdd0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd4ae85ce60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd4ae85cef0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd4ae8ad840>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False]",
+        "_np_random": null,
+        "_shape": [
+            24
+        ]
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "low": "[-1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_np_random": "RandomState(MT19937)",
+        "_shape": [
+            4
+        ]
+    },
+    "n_envs": 16,
+    "num_timesteps": 100007936,
+    "_total_timesteps": 100000000,
+    "_num_timesteps_at_start": 0,
+    "seed": 0,
+    "action_noise": null,
+    "start_time": 1614946046.6976352,
+    "learning_rate": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": null,
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -7.935999999997279e-05,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 30520,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.001,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "_last_dones": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    }
+}

ppo-BipedalWalkerHardcore-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0729a01e4094e118f13f5aa4cf5257f5aa4455e3c75c54092a3a78d9e680a908
+size 101783

ppo-BipedalWalkerHardcore-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:256a1f91a43e858c1df2837adda1d5698f1322925cdad1e8cd025b2112afdb0a
+size 51710

ppo-BipedalWalkerHardcore-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-BipedalWalkerHardcore-v3/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.13.0-44-generic-x86_64-with-debian-bullseye-sid #49~20.04.1-Ubuntu SMP Wed May 18 18:44:28 UTC 2022
+Python: 3.7.10
+Stable-Baselines3: 1.5.1a8
+PyTorch: 1.11.0
+GPU Enabled: True
+Numpy: 1.21.2
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f57cc49074b04b697c02b5690357cf6ad593abccd98f47efdf84501d4f6b1301
+size 460935

results.json

@@ -0,0 +1 @@
+{"mean_reward": 48.6052106, "std_reward": 120.6809115045838, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-02T14:44:25.224838"}

train_eval_metrics.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fc1d75bff2716ce9040cab53034c4be3cb94ee00d4638dcff0b87362673ec21
+size 2479767

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b440be467bf33c0a474ea1cd4c5ab461a167e777b5bee2f55555e28d432baa8a
+size 6496