Upload PPO LunarLander-v2 trained agent

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 257.33 +/- 18.46
+      value: -133.88 +/- 35.82
       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 0x7f3a7d175f80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3a7d17d050>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3a7d17d0e0>", "_build_mlp_extractor": "<function 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ppo-LunarLander-v2/data

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"
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 168,
+    "_n_updates": 16,
     "n_steps": 1024,
     "gamma": 0.999,
-    "gae_lambda": 0.98,
-    "ent_coef": 0.01,
+    "gae_lambda": 0.99,
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results.json

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