more training steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: BipedalWalker-v3
     metrics:
     - type: mean_reward
-      value: 239.93 +/- 114.24
+      value: 200.13 +/- 145.83
       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 0x16df2b9a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16df2ba30>", "reset_noise": "<function 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ppo-BipedalWalker-v3.zip

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ppo-BipedalWalker-v3/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 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 0x16df2bf40>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x16df34040>",
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+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x1437d7ac0>",
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         "__abstractmethods__": "frozenset()",
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     "verbose": 1,
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     "_last_episode_starts": {
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results.json

@@ -1 +1 @@
-{"mean_reward": 239.93290389429603, "std_reward": 114.23728262623825, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-24T18:15:31.268668"}
+{"mean_reward": 200.1335456501336, "std_reward": 145.8333287462424, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-24T18:17:51.355739"}