more training steps

README.md

@@ -16,7 +16,7 @@ model-index:
       type: BipedalWalker-v3
     metrics:
     - type: mean_reward
-      value: 117.54 +/- 61.72
+      value: 239.93 +/- 114.24
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x16df2b9a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x16df2ba30>", "reset_noise": "<function 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     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -75,13 +75,13 @@
     "_current_progress_remaining": -0.02400000000000002,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 80,
+    "_n_updates": 240,
     "n_steps": 1024,
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results.json

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