a2c-PandaReachDense-v3 / config.json
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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 0x00000164E43EEC00>", "__static_attributes__": [], "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x00000164E43F04C0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 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