Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -829.34 +/- 341.30
+      value: -182.38 +/- 138.23
       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 0x7fcbe8646e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcbe8646ee0>", "reset_noise": 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"
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     "ep_success_buffer": {
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-    "_n_updates": 980,
-    "n_steps": 1024,
-    "gamma": 0.9317539296272632,
-    "gae_lambda": 0.9182480658673623,
-    "ent_coef": 0.04836019970542158,
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-- Python: 3.8.10
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