Upload PPO BipedalWalker-v3 trained agent

BipedalWalker-v3.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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BipedalWalker-v3/data

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     "seed": null,
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     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
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BipedalWalker-v3/policy.optimizer.pth

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 size 105121

BipedalWalker-v3/policy.pth

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 version https://git-lfs.github.com/spec/v1
-oid sha256:19c71bfbc386ad6eaf2f44f9a30832f74e078a5c604dd439ccf3b4435ef97d42
+oid sha256:3dd0044890cd9130ba842e271c1f653debc9adc298a2ee14add21d220c7d43d5
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README.md

@@ -16,7 +16,7 @@ model-index:
       type: BipedalWalker-v3
     metrics:
     - type: mean_reward
-      value: -26.06 +/- 10.56
+      value: 118.81 +/- 2.73
       name: mean_reward
       verified: false
 ---

config.json

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-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7e38025c36d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e38025c3760>", "reset_noise": 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results.json

@@ -1 +1 @@
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