improved a rl agent on the LunarLander with PPO.

LunarLanderPPO.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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LunarLanderPPO/data

@@ -21,17 +21,17 @@
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     "policy_kwargs": {},
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LunarLanderPPO/policy.optimizer.pth

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LunarLanderPPO/policy.pth

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README.md

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       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 216.90 +/- 78.75
+      value: 268.65 +/- 16.60
       name: mean_reward
       verified: false
 ---

config.json

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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 0x7950ef67d870>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7950ef67d900>", "reset_noise": 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results.json

@@ -1 +1 @@
-{"mean_reward": 216.90424789999997, "std_reward": 78.75087660966558, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-12T10:29:20.099378"}
+{"mean_reward": 268.64690429999996, "std_reward": 16.600407013148676, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-12T12:02:03.119356"}