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Upload DQN MountainCar-v0 trained agent
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{
"policy_class": {
":type:": "<class 'abc.ABCMeta'>",
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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 0x7f7285183440>",
"_build": "<function DQNPolicy._build at 0x7f72851834d0>",
"make_q_net": "<function DQNPolicy.make_q_net at 0x7f7285183560>",
"forward": "<function DQNPolicy.forward at 0x7f72851835f0>",
"_predict": "<function DQNPolicy._predict at 0x7f7285183680>",
"_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f7285183710>",
"set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f72851837a0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7f7285165f90>"
},
"verbose": 1,
"policy_kwargs": {
"net_arch": [
256,
256
]
},
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"low": "[-1.2 -0.07]",
"high": "[0.6 0.07]",
"bounded_below": "[ True True]",
"bounded_above": "[ True True]",
"_np_random": null
},
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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 :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 ",
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"__abstractmethods__": "frozenset()",
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