Uploading PPO trained agent

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 258.56 +/- 17.29
+      value: 239.42 +/- 71.12
       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 0x7fd4fe50d8c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd4fe50d950>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd4fe50d9e0>", "_build_mlp_extractor": "<function 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"
+        ":serialized:": 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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -82,7 +82,7 @@
     "ent_coef": 0.01,
     "vf_coef": 0.5,
     "max_grad_norm": 0.5,
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