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Browse files- README.md +1 -1
- a2c-PandaReachDense-v2.zip +2 -2
- a2c-PandaReachDense-v2/data +8 -8
- a2c-PandaReachDense-v2/policy.optimizer.pth +1 -1
- a2c-PandaReachDense-v2/policy.pth +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- vec_normalize.pkl +1 -1
README.md
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -
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name: mean_reward
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verified: false
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---
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type: PandaReachDense-v2
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metrics:
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- type: mean_reward
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value: -5.78 +/- 1.35
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name: mean_reward
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verified: false
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---
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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: 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 0x7fef0ca2c700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fef0ca22d40>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": 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