2. Upload PPO LunarLander-v2 trained agent

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -191.27 +/- 66.80
+      value: 272.13 +/- 19.88
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -1 +1 @@
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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 0x7fe06c059170>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe06c059200>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe06c059290>", "_build_mlp_extractor": "<function 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+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 52,
+    "_n_updates": 300,
     "n_steps": 1024,
     "gamma": 0.999,
     "gae_lambda": 0.98,

ppo-LunarLander-v2/policy.optimizer.pth

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ppo-LunarLander-v2/policy.pth

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replay.mp4

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results.json

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