trained model 2e+06 steps

LunarLander-v2-ppo.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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LunarLander-v2-ppo/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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+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbcb39ed950>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbcb39ed9e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbcb39eda70>",
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         "__abstractmethods__": "frozenset()",
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"
+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -89,7 +86,7 @@
     "n_epochs": 10,
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LunarLander-v2-ppo/policy.optimizer.pth

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LunarLander-v2-ppo/policy.pth

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README.md

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   results:
   - metrics:
     - type: mean_reward
-      value: 286.78 +/- 14.96
+      value: 297.26 +/- 14.96
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

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