ib1368 commited on
Commit
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1 Parent(s): 08854cb

Upload PPO LunarLander-v2 trained agent

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README.md CHANGED
@@ -16,7 +16,7 @@ model-index:
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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- value: 270.37 +/- 13.15
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  name: mean_reward
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  verified: false
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  ---
 
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  type: LunarLander-v2
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  metrics:
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  - type: mean_reward
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+ value: 288.56 +/- 14.40
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  name: mean_reward
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  verified: false
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  ---
config.json CHANGED
@@ -1 +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 0x7d930f846950>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d930f8469e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d930f846a70>", 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@@ -1 +1 @@
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- {"mean_reward": 270.3651882271363, "std_reward": 13.154684396584425, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-06T15:52:00.703057"}
 
1
+ {"mean_reward": 288.5580851472774, "std_reward": 14.402199110974719, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-19T07:37:38.245012"}