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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 0x72f4ce755620>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x72f4ce7556c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x72f4ce755760>", 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