RL-course unit 1 exercise

PPO-rlcourse.zip

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PPO-rlcourse/data

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PPO-rlcourse/policy.pth

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README.md

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       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 128.15 +/- 125.57
+      value: 286.85 +/- 18.22
       name: mean_reward
       verified: false
 ---

config.json

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If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). 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 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 0x7fb3f112af70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb3f112f040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb3f112f0d0>", "_build_mlp_extractor": "<function 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results.json

@@ -1 +1 @@
-{"mean_reward": 128.1522693503921, "std_reward": 125.56858512603914, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-25T16:36:45.987349"}
+{"mean_reward": 286.85052865084776, "std_reward": 18.223468854509253, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-25T17:02:21.127129"}