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{
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"__module__": "sb3_contrib.tqc.policies",
"__doc__": "\n Policy class (with both actor and critic) for TQC.\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 feature 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_quantiles: Number of quantiles for the critic.\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 ",
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"make_actor": "<function TQCPolicy.make_actor at 0x7fb0c9d2a8b0>",
"make_critic": "<function TQCPolicy.make_critic at 0x7fb0c9d2a940>",
"forward": "<function TQCPolicy.forward at 0x7fb0c9d2a9d0>",
"_predict": "<function TQCPolicy._predict at 0x7fb0c9d2aa60>",
"set_training_mode": "<function TQCPolicy.set_training_mode at 0x7fb0c9d2aaf0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7fb0c9d27fc0>"
},
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"buffer_size": 1,
"batch_size": 256,
"learning_starts": 10000,
"tau": 0.005,
"gamma": 0.99,
"gradient_steps": 1,
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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 0x7fb0c9f2e5e0>",
"add": "<function ReplayBuffer.add at 0x7fb0c9f2e670>",
"sample": "<function ReplayBuffer.sample at 0x7fb0c9f2e700>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7fb0c9f2e790>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7fb0c9f26640>"
},
"replay_buffer_kwargs": {},
"train_freq": {
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
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"use_sde_at_warmup": false,
"target_entropy": -6.0,
"ent_coef": "auto",
"target_update_interval": 1,
"top_quantiles_to_drop_per_net": 2,
"batch_norm_stats": [],
"batch_norm_stats_target": []
} |