Upload model: PPO-LunarLander-v2, version: 3.000000

PPO-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 143996
+oid sha256:26930afaf66bbc510e51af32b984615803643147d1bf0931b719f8de1d1be18e
+size 143992

PPO-LunarLander-v2/data

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     "seed": null,
     "action_noise": null,
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     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
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PPO-LunarLander-v2/policy.optimizer.pth

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PPO-LunarLander-v2/policy.pth

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README.md

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   results:
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       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 0x7fcf5c4725f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf5c472680>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf5c472710>", "_build_mlp_extractor": "<function 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