Improve PPO trained agent to 276.44 +/- 21.651967758051594

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 271.14 +/- 17.70
+      value: 255.13 +/- 64.66
       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 0x7ff40e7453f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff40e745480>", "reset_noise": 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It allows to keep variance\n        above zero and prevent it from growing too fast. 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ppo-LunarLander-v2/policy.pth

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 size 43329

results.json

@@ -1 +1 @@
-{"mean_reward": 271.14413681819343, "std_reward": 17.70331356061353, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-12T03:01:23.988839"}
+{"mean_reward": 255.12738523909792, "std_reward": 64.65579970572723, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-12T03:35:36.942244"}