DQN trained with rl-baselines3-zoo.

d9ecf81 about 2 years ago

No virus

18.9 kB

	{
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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 ",
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	"_build": "<function DQNPolicy._build at 0x7ff47da14158>",
	"make_q_net": "<function DQNPolicy.make_q_net at 0x7ff47da141e0>",
	"forward": "<function DQNPolicy.forward at 0x7ff47da14268>",
	"_predict": "<function DQNPolicy._predict at 0x7ff47da142f0>",
	"_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7ff47da14378>",
	"set_training_mode": "<function DQNPolicy.set_training_mode at 0x7ff47da14400>",
	"__abstractmethods__": "frozenset()",
	"_abc_registry": "<_weakrefset.WeakSet object at 0x7ff47da0ce48>",
	"_abc_cache": "<_weakrefset.WeakSet object at 0x7ff47da0ce80>",
	"_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7ff47da0cef0>",
	"_abc_negative_cache_version": 59
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	"verbose": 1,
	"policy_kwargs": {
	"net_arch": [
	256,
	256
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	"dtype": "float32",
	"shape": [
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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
	},
	"action_space": {
	":type:": "<class 'gym.spaces.discrete.Discrete'>",
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	"batch_size": 128,
	"learning_starts": 1000,
	"tau": 1.0,
	"gamma": 0.98,
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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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	"add": "<function ReplayBuffer.add at 0x7ff47da5c378>",
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	"__abstractmethods__": "frozenset()",
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	"_abc_negative_cache": "<_weakrefset.WeakSet object at 0x7ff47db41438>",
	"_abc_negative_cache_version": 59
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	"replay_buffer_kwargs": {},
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	"use_sde_at_warmup": false,
	"exploration_initial_eps": 1.0,
	"exploration_final_eps": 0.07,
	"exploration_fraction": 0.2,
	"target_update_interval": 600,
	"max_grad_norm": 10,
	"exploration_rate": 0.07,
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