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| "policy_class": { | |
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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 0x7f5d5f7cb4c0>", | |
| "_build": "<function DQNPolicy._build at 0x7f5d5f7cb550>", | |
| "make_q_net": "<function DQNPolicy.make_q_net at 0x7f5d5f7cb5e0>", | |
| "forward": "<function DQNPolicy.forward at 0x7f5d5f7cb670>", | |
| "_predict": "<function DQNPolicy._predict at 0x7f5d5f7cb700>", | |
| "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f5d5f7cb790>", | |
| "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f5d5f7cb820>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f5d5f7ce040>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": { | |
| "net_arch": [ | |
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| }, | |
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| "high": "[ 1. 1. 1. 1. 12.566371 28.274334]", | |
| "bounded_below": "[ True True True True True True]", | |
| "bounded_above": "[ True True True True True True]", | |
| "_np_random": null | |
| }, | |
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| ":type:": "<class 'gym.spaces.discrete.Discrete'>", | |
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| "ep_success_buffer": { | |
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| }, | |
| "_n_updates": 100000, | |
| "buffer_size": 1, | |
| "batch_size": 128, | |
| "learning_starts": 0, | |
| "tau": 1.0, | |
| "gamma": 0.99, | |
| "gradient_steps": -1, | |
| "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: PyTorch 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 0x7f5d5f82a430>", | |
| "add": "<function ReplayBuffer.add at 0x7f5d5f82a4c0>", | |
| "sample": "<function ReplayBuffer.sample at 0x7f5d5f82a550>", | |
| "_get_samples": "<function ReplayBuffer._get_samples at 0x7f5d5f82a5e0>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f5d5f821900>" | |
| }, | |
| "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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| }, | |
| "batch_norm_stats": [], | |
| "batch_norm_stats_target": [] | |
| } |