Third commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: PandaReachDense-v2
     metrics:
     - type: mean_reward
-      value: -11.31 +/- 4.50
+      value: -4.93 +/- 1.44
       name: mean_reward
       verified: false
 ---

a2c-PandaReachDense-v2.zip

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a2c-PandaReachDense-v2/data

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a2c-PandaReachDense-v2/policy.pth

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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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7f1b0f382b90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f1b0f18d880>"}, "verbose": 1, 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results.json

@@ -1 +1 @@
-{"mean_reward": -11.31293875798583, "std_reward": 4.4974892922215925, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-07T15:27:35.031346"}
+{"mean_reward": -4.925284001184627, "std_reward": 1.4446284691932747, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-08T03:05:38.519116"}

vec_normalize.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:300868e06ac276c0a604228988704c77effa4e57217e8f28fb5bbadd7c782016
 size 2387