Upload PPO LunarLander-v2 trained agent

PPO_LunarLander.zip

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PPO_LunarLander/data

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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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-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6d1d904a70>",
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-        "_build": "<function ActorCriticPolicy._build at 0x7f6d1d904b90>",
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+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3e70676b90>",
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         "__abstractmethods__": "frozenset()",
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PPO_LunarLander/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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PPO_LunarLander/policy.pth

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PPO_LunarLander/system_info.txt

@@ -4,4 +4,4 @@ Stable-Baselines3: 1.5.0
 PyTorch: 1.11.0+cu113
 GPU Enabled: True
 Numpy: 1.21.6
-Gym: 0.17.3
+Gym: 0.21.0

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 239.98 +/- 63.23
+      value: 271.74 +/- 12.54
       name: mean_reward
     task:
       type: reinforcement-learning

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 0x7f6d1d904950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6d1d9049e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6d1d904a70>", "_build_mlp_extractor": "<function 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replay.mp4

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+oid sha256:d581cdfa8752b0bcb742753ba0b5dd4fa9fcb1baa7f727722e24306acde33655
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results.json

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