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"_shape": [],
"dtype": "int64",
"_np_random": "RandomState(MT19937)"
},
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"buffer_size": 1000000,
"batch_size": 32,
"learning_starts": 50000,
"tau": 1.0,
"gamma": 0.99,
"gradient_steps": 1,
"optimize_memory_usage": false,
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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: 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 ",
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"add": "<function ReplayBuffer.add at 0x7f635e1d80d0>",
"sample": "<function ReplayBuffer.sample at 0x7f635e1d8160>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7f635e1d81f0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7f635e1d7380>"
},
"replay_buffer_kwargs": {},
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":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
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"exploration_initial_eps": 1.0,
"exploration_final_eps": 0.05,
"exploration_fraction": 0.1,
"target_update_interval": 39,
"_n_calls": 626652,
"max_grad_norm": 10,
"exploration_rate": 0.05,
"batch_norm_stats": [],
"batch_norm_stats_target": [],
"exploration_schedule": {
":type:": "<class 'function'>",
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