Upload PPO trained agent

Lunar_Lander_PPO_v1.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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Lunar_Lander_PPO_v1/data

@@ -21,17 +21,17 @@
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     "policy_kwargs": {},
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     "seed": null,
     "action_noise": null,
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README.md

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       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 253.01 +/- 13.61
+      value: 274.29 +/- 15.83
       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 0x7b9f2641cb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b9f2641cc10>", "reset_noise": 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results.json

@@ -1 +1 @@
-{"mean_reward": 253.0106153, "std_reward": 13.607263560117259, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-22T20:34:08.231352"}
+{"mean_reward": 274.2876833000001, "std_reward": 15.831148448406228, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-22T21:11:01.758105"}