Initial commit

.gitattributes

@@ -25,3 +25,4 @@ 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
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,56 +1,36 @@
 ---
+library_name: stable-baselines3
 tags:
+- Walker2DBulletEnv-v0
 - deep-reinforcement-learning
 - reinforcement-learning
 - stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 35.11 +/- 4.51
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Walker2DBulletEnv-v0
+      type: Walker2DBulletEnv-v0
 ---
-# ppo-Walker2DBulletEnv-v0
 
-This is a pre-trained model of a PPO agent playing Walker2DBulletEnv-v0 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
+# **PPO** Agent playing **Walker2DBulletEnv-v0**
+This is a trained model of a **PPO** agent playing **Walker2DBulletEnv-v0**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
-### Usage (with Stable-baselines3)
-Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
-```
-pip install stable-baselines3
-pip install huggingface_sb3
-```
+## Usage (with Stable-baselines3)
+TODO: Add your code
 
-Then, you can use the model like this:
 
 ```python
-
-import gym
-import pybullet_envs
-
+from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
 
-from stable_baselines3 import PPO
-from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
-from stable_baselines3.common.evaluation import evaluate_policy
-
-# Retrieve the model from the hub
-## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
-## filename = name of the model zip file from the repository
-repo_id = "ThomasSimonini/ppo-Walker2DBulletEnv-v0"
-checkpoint = load_from_hub(repo_id = repo_id, filename="ppo-Walker2DBulletEnv-v0.zip")
-model = PPO.load(checkpoint)
-
-# Load the saved statistics
-stats_path = load_from_hub(repo_id = repo_id, filename="vec_normalize.pkl")
-
-eval_env = DummyVecEnv([lambda: gym.make("Walker2DBulletEnv-v0")])
-eval_env = VecNormalize.load(stats_path, eval_env)
-#  do not update them at test time
-eval_env.training = False
-# reward normalization is not needed at test time
-eval_env.norm_reward = False
-
-from stable_baselines3.common.evaluation import evaluate_policy
-
-mean_reward, std_reward = evaluate_policy(model, eval_env)
-print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
-
+...
 ```
-
-### Evaluation Results
-Mean_reward: 2371.90 +/- 16.50

config.json

@@ -0,0 +1 @@
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ppo-Walker2DBulletEnv-v0/_stable_baselines3_version

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ppo-Walker2DBulletEnv-v0/data

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+{
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+        "__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 0x7f8c03269680>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8c03269710>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8c032697a0>",
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+        "__abstractmethods__": "frozenset()",
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