Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
  type: LunarLander-v2
  metrics:
  - type: mean_reward
- value: 280.04 +/- 17.38
+ value: 81.08 +/- 57.43
  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 0x7cb1e7eb0670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cb1e7eb0700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7cb1e7eb0790>", 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It allows to keep variance\n above zero and prevent it from growing too fast. 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