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{
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"__module__": "sb3_contrib.qrdqn.policies",
"__doc__": "\n Policy class with quantile and target networks for QR-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 n_quantiles: Number of quantiles\n :param net_arch: The specification of the network architecture.\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 QRDQNPolicy.__init__ at 0x7fc6de66dc20>",
"_build": "<function QRDQNPolicy._build at 0x7fc6de66dcb0>",
"make_quantile_net": "<function QRDQNPolicy.make_quantile_net at 0x7fc6de66dd40>",
"forward": "<function QRDQNPolicy.forward at 0x7fc6de66ddd0>",
"_predict": "<function QRDQNPolicy._predict at 0x7fc6de66de60>",
"_get_constructor_parameters": "<function QRDQNPolicy._get_constructor_parameters at 0x7fc6de66def0>",
"set_training_mode": "<function QRDQNPolicy.set_training_mode at 0x7fc6de66df80>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7fc6de66c1b0>"
},
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],
"n_quantiles": 10,
"optimizer_class": "<class 'torch.optim.adam.Adam'>",
"optimizer_kwargs": {
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}
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"high": "[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]",
"bounded_below": "[ True True True True]",
"bounded_above": "[ True True True True]",
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},
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"buffer_size": 1,
"batch_size": 64,
"learning_starts": 1000,
"tau": 1.0,
"gamma": 0.99,
"gradient_steps": 128,
"optimize_memory_usage": false,
"replay_buffer_class": {
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"__module__": "stable_baselines3.common.buffers",
"__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 0x7fc6deec47a0>",
"add": "<function ReplayBuffer.add at 0x7fc6deec4830>",
"sample": "<function ReplayBuffer.sample at 0x7fc6deeb1830>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7fc6deeb18c0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7fc6def2c060>"
},
"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": 0.04,
"exploration_fraction": 0.16,
"target_update_interval": 10,
"max_grad_norm": null,
"exploration_rate": 0.04,
"exploration_schedule": {
":type:": "<class 'function'>",
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"n_quantiles": 10
} |