Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 222.01 +/- 58.82
+      value: 264.00 +/- 25.45
       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 0x7f885bf15e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f885bf15ee0>", "reset_noise": 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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 0x7f30a66411f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f30a6641280>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/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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"
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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 5864,
+    "_n_updates": 11720,
     "n_steps": 1024,
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results.json

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