dqn-CartPole-v1_2 / config.json
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Upload DQN CartPole-v trained agent
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``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 0x000002BC8E39A0E0>", "_build": "<function DQNPolicy._build at 0x000002BC8E39A170>", "make_q_net": "<function DQNPolicy.make_q_net at 0x000002BC8E39A200>", "forward": "<function DQNPolicy.forward at 0x000002BC8E39A290>", "_predict": "<function DQNPolicy._predict at 0x000002BC8E39A320>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x000002BC8E39A3B0>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x000002BC8E39A440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000002BC8E3AABC0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 50176, "_total_timesteps": 50000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713559494965558700, "learning_rate": 0.003, "tensorboard_log": 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"__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}", "__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 0x000002BC8E0B3130>", "add": "<function ReplayBuffer.add at 0x000002BC8E0B31C0>", "sample": "<function ReplayBuffer.sample at 0x000002BC8E0B3250>", "_get_samples": "<function ReplayBuffer._get_samples at 0x000002BC8E0B32E0>", "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x000002BC8E0B3370>)>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000002BC8B8AD700>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYgAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RNAAFoAIwSVHJhaW5GcmVxdWVuY3lVbml0lJOUjARzdGVwlIWUUpSGlIGULg=="}, 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