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| "policy_class": { | |
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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 0x7f6db57d6700>", | |
| "_build": "<function SACPolicy._build at 0x7f6db57d6790>", | |
| "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f6db57d6820>", | |
| "reset_noise": "<function SACPolicy.reset_noise at 0x7f6db57d68b0>", | |
| "make_actor": "<function SACPolicy.make_actor at 0x7f6db57d6940>", | |
| "make_critic": "<function SACPolicy.make_critic at 0x7f6db57d69d0>", | |
| "forward": "<function SACPolicy.forward at 0x7f6db57d6a60>", | |
| "_predict": "<function SACPolicy._predict at 0x7f6db57d6af0>", | |
| "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f6db57d6b80>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc_data object at 0x7f6db57cdb10>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": { | |
| "net_arch": [ | |
| 256, | |
| 256 | |
| ], | |
| "log_std_init": -2.2692589009754176, | |
| "use_sde": false | |
| }, | |
| "num_timesteps": 1000000, | |
| "_total_timesteps": 1000000, | |
| "_num_timesteps_at_start": 0, | |
| "seed": 0, | |
| "action_noise": null, | |
| "start_time": 1694771152660236841, | |
| "learning_rate": { | |
| ":type:": "<class 'function'>", | |
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| }, | |
| "replay_buffer_kwargs": {}, | |
| "train_freq": { | |
| ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", | |
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| }, | |
| "use_sde_at_warmup": false, | |
| "target_entropy": -8.0, | |
| "ent_coef": "auto", | |
| "target_update_interval": 1, | |
| "lr_schedule": { | |
| ":type:": "<class 'function'>", | |
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| }, | |
| "batch_norm_stats": [], | |
| "batch_norm_stats_target": [] | |
| } |