trained model 2e+06 steps

LunarLander-v2-ppo.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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LunarLander-v2-ppo/data

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LunarLander-v2-ppo/policy.optimizer.pth

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LunarLander-v2-ppo/policy.pth

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README.md

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   results:
   - metrics:
     - type: mean_reward
-      value: 274.96 +/- 15.47
+      value: 286.78 +/- 14.96
       name: mean_reward
     task:
       type: reinforcement-learning

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 0x7f2ecf22b710>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2ecf22b7a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2ecf22b830>", "_build_mlp_extractor": "<function 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replay.mp4

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 version https://git-lfs.github.com/spec/v1
-oid sha256:59454b2cee1b147653b6b233ee2f69d7dece2a6533ec9885aad9321e550a94af
-size 200437
+oid sha256:1a29c0ab561de0e423f12fda37095568bbff49b5133e50f159ea005e5434495d
+size 183843

results.json

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-{"mean_reward": 274.958423, "std_reward": 15.466405560474827, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-13T20:47:02.517261"}
+{"mean_reward": 286.77860150000004, "std_reward": 14.959645769944784, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-13T21:27:48.523275"}