Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 245.46 +/- 68.97
+      value: 253.41 +/- 14.34
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 0x7f6c416b8dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6c416b8e50>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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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 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_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6c416b9360>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6c416b93f0>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c991ce2bd90>",
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+        "forward": "<function ActorCriticPolicy.forward at 0x7c991ce2bf40>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c991ce30040>",
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+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c991ce301f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c991ce30280>",
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         "__abstractmethods__": "frozenset()",
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ppo-LunarLander-v2/policy.optimizer.pth

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ppo-LunarLander-v2/policy.pth

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ppo-LunarLander-v2/system_info.txt

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 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.3.0+cu121
-- GPU Enabled: True
+- GPU Enabled: False
 - Numpy: 1.25.2
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1

results.json

@@ -1 +1 @@
-{"mean_reward": 245.46417119999995, "std_reward": 68.96655443902499, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-29T21:04:11.221003"}
+{"mean_reward": 253.4123265, "std_reward": 14.342435703238973, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-30T07:51:11.897278"}