BipedalWalker v1

README.md

@@ -1,7 +1,7 @@
 ---
 library_name: stable-baselines3
 tags:
-- LunarLander-v2
+- BipedalWalker-v3
 - deep-reinforcement-learning
 - reinforcement-learning
 - stable-baselines3
@@ -12,17 +12,17 @@ model-index:
       type: reinforcement-learning
       name: reinforcement-learning
     dataset:
-      name: LunarLander-v2
-      type: LunarLander-v2
+      name: BipedalWalker-v3
+      type: BipedalWalker-v3
     metrics:
     - type: mean_reward
-      value: 287.21 +/- 25.48
+      value: -22.95 +/- 48.84
       name: mean_reward
       verified: false
 ---
 
-# **PPO** Agent playing **LunarLander-v2**
-This is a trained model of a **PPO** agent playing **LunarLander-v2**
+# **PPO** Agent playing **BipedalWalker-v3**
+This is a trained model of a **PPO** agent playing **BipedalWalker-v3**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
 
 ## Usage (with Stable-baselines3)

bipedalV1.zip

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0581e7d367f9601c53f64927bf321a0add7a85423fab22b7aca7f00e4f9264e2
+size 175232

bipedalV1/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

bipedalV1/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    ",
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+        "forward": "<function ActorCriticPolicy.forward at 0x7f703d79a4c0>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f703d79a670>",
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+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f703d79a790>",
+        "__abstractmethods__": "frozenset()",
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bipedalV1/system_info.txt

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+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

config.json

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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 0x7f0ff591e3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0ff591e430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0ff591e4c0>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"mean_reward": 287.2072845970215, "std_reward": 25.48408912337809, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-19T20:02:58.861420"}
+{"mean_reward": -22.949079933209212, "std_reward": 48.84285536944668, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-19T21:47:20.716714"}