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"__module__": "stable_baselines3.dqn.policies",
"__doc__": "\n Policy class with Q-Value Net and target net for DQN\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\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 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 DQNPolicy.__init__ at 0x7f6263d219e0>",
"_build": "<function DQNPolicy._build at 0x7f6263d21a70>",
"make_q_net": "<function DQNPolicy.make_q_net at 0x7f6263d21b00>",
"forward": "<function DQNPolicy.forward at 0x7f6263d21b90>",
"_predict": "<function DQNPolicy._predict at 0x7f6263d21c20>",
"_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f6263d21cb0>",
"set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f6263d21d40>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7f6263d098d0>"
},
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]
},
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"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
"high": "[inf inf inf inf inf inf inf inf]",
"bounded_below": "[False False False False False False False False]",
"bounded_above": "[False False False False False False False False]",
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},
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"batch_size": 128,
"learning_starts": 0,
"tau": 1.0,
"gamma": 0.99,
"gradient_steps": -1,
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"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device:\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
"__init__": "<function ReplayBuffer.__init__ at 0x7f62641987a0>",
"add": "<function ReplayBuffer.add at 0x7f6264198830>",
"sample": "<function ReplayBuffer.sample at 0x7f6264185830>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7f62641858c0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7f6264200060>"
},
"replay_buffer_kwargs": {},
"train_freq": {
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
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"actor": null,
"use_sde_at_warmup": false,
"exploration_initial_eps": 1.0,
"exploration_final_eps": 0.1,
"exploration_fraction": 0.12,
"target_update_interval": 250,
"_n_calls": 100000,
"max_grad_norm": 10,
"exploration_rate": 0.1,
"exploration_schedule": {
":type:": "<class 'function'>",
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} |