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"__module__": "stable_baselines3.td3.policies",
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},
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"buffer_size": 1,
"batch_size": 100,
"learning_starts": 10000,
"tau": 0.005,
"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 0x7f0df91ae5e0>",
"add": "<function ReplayBuffer.add at 0x7f0df91ae670>",
"sample": "<function ReplayBuffer.sample at 0x7f0df91ae700>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7f0df91ae790>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7f0df91a85c0>"
},
"replay_buffer_kwargs": {},
"train_freq": {
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
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"use_sde_at_warmup": false,
"policy_delay": 2,
"target_noise_clip": 0.5,
"target_policy_noise": 0.2,
"actor_batch_norm_stats": [],
"critic_batch_norm_stats": [],
"actor_batch_norm_stats_target": [],
"critic_batch_norm_stats_target": []
} |