Upload PPO LunarLander-v2 trained agent

README.md

@@ -8,57 +8,30 @@ tags:
 model-index:
 - name: PPO
   results:
-  - metrics:
-    - type: mean_reward
-      value: 271.51 +/- 16.73
-      name: mean_reward
-    task:
+  - task:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
       name: LunarLander-v2
       type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: -135.43 +/- 104.92
+      name: mean_reward
+      verified: false
 ---
-# ppo-LunarLander-v2
-
-This is a pre-trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library.
 
-### Usage (with Stable-baselines3)
-Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
-```
-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
-
+from stable_baselines3 import ...
 from huggingface_sb3 import load_from_hub
-from stable_baselines3 import PPO
-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
-checkpoint = load_from_hub(repo_id="ThomasSimonini/ppo-LunarLander-v2", filename="ppo-LunarLander-v2.zip")
-model = PPO.load(checkpoint)
-
-# Evaluate the agent
-eval_env = gym.make('LunarLander-v2')
-mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
-print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
- 
-# Watch the agent play
-obs = eval_env.reset()
-for i in range(1000):
-    action, _state = model.predict(obs)
-    obs, reward, done, info = eval_env.step(action)
-    eval_env.render()
-    if done:
-        obs = eval_env.reset()
-eval_env.close()
+...
 ```
-

config.json

@@ -1 +1 @@
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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 0x7f41d9183b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f41d9183b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f41d9183c20>", "_build_mlp_extractor": "<function 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+size 147276

ppo-LunarLander-v2/_stable_baselines3_version

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ppo-LunarLander-v2/data

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-        "__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    ",
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-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f41d91080e0>",
+        "__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    ",
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+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc037fae160>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc037fae1f0>",
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+        "_build": "<function ActorCriticPolicy._build at 0x7fc037fae310>",
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+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc037fae430>",
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+        "_predict": "<function ActorCriticPolicy._predict at 0x7fc037fae550>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc037fae5e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc037fae670>",
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         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f41d9148e10>"
+        "_abc_impl": "<_abc._abc_data object at 0x7fc037fadfc0>"
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     "verbose": 1,
     "policy_kwargs": {},
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     "seed": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
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@@ -63,27 +67,27 @@
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     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -0.015808000000000044,
+    "_current_progress_remaining": -15.384,
     "ep_info_buffer": {
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