| { | |
| "policy_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
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| "__module__": "stable_baselines3.sac.policies", | |
| "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}", | |
| "__doc__": "\n Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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 :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", | |
| "__init__": "<function SACPolicy.__init__ at 0x7f3bd460ce00>", | |
| "_build": "<function SACPolicy._build at 0x7f3bd460d3a0>", | |
| "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f3bd460d440>", | |
| "reset_noise": "<function SACPolicy.reset_noise at 0x7f3bd460d4e0>", | |
| "make_actor": "<function SACPolicy.make_actor at 0x7f3bd460d580>", | |
| "make_critic": "<function SACPolicy.make_critic at 0x7f3bd460d620>", | |
| "forward": "<function SACPolicy.forward at 0x7f3bd460d6c0>", | |
| "_predict": "<function SACPolicy._predict at 0x7f3bd460d760>", | |
| "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f3bd460d800>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f3bd4607f40>" | |
| }, | |
| "verbose": 0, | |
| "policy_kwargs": { | |
| "use_sde": false | |
| }, | |
| "num_timesteps": 1990000, | |
| "_total_timesteps": 2000000, | |
| "_num_timesteps_at_start": 0, | |
| "seed": 0, | |
| "action_noise": null, | |
| "start_time": 1737802112906651827, | |
| "learning_rate": 0.0003, | |
| "tensorboard_log": "runs/0", | |
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| }, | |
| "_n_updates": 395999, | |
| "buffer_size": 1000000, | |
| "batch_size": 256, | |
| "learning_starts": 10000, | |
| "tau": 0.005, | |
| "gamma": 0.99, | |
| "gradient_steps": 1, | |
| "optimize_memory_usage": false, | |
| "replay_buffer_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
| ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", | |
| "__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 0x7f3bdf528d60>", | |
| "add": "<function ReplayBuffer.add at 0x7f3bdf528ea0>", | |
| "sample": "<function ReplayBuffer.sample at 0x7f3bdf528f40>", | |
| "_get_samples": "<function ReplayBuffer._get_samples at 0x7f3bdf528fe0>", | |
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| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x7f3bdf51db80>" | |
| }, | |
| "replay_buffer_kwargs": {}, | |
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| "target_update_interval": 1, | |
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| "lr_schedule": { | |
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| "batch_norm_stats_target": [] | |
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