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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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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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| "batch_size": 256, | |
| "learning_starts": 100, | |
| "tau": 0.005, | |
| "gamma": 0.99, | |
| "gradient_steps": 1, | |
| "optimize_memory_usage": false, | |
| "replay_buffer_class": { | |
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| "__module__": "stable_baselines3.common.buffers", | |
| "__doc__": "\n Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n Extends the ReplayBuffer to use dictionary observations\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 Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\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 DictReplayBuffer.__init__ at 0x7f4e2f29c4c0>", | |
| "add": "<function DictReplayBuffer.add at 0x7f4e2f29c550>", | |
| "sample": "<function DictReplayBuffer.sample at 0x7f4e2f29c5e0>", | |
| "_get_samples": "<function DictReplayBuffer._get_samples at 0x7f4e2f29c670>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc_data object at 0x7f4e2f314e10>" | |
| }, | |
| "replay_buffer_kwargs": {}, | |
| "train_freq": { | |
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| "use_sde_at_warmup": false, | |
| "target_entropy": { | |
| ":type:": "<class 'numpy.float32'>", | |
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| }, | |
| "ent_coef": "auto", | |
| "target_update_interval": 1, | |
| "top_quantiles_to_drop_per_net": 2, | |
| "batch_norm_stats": [], | |
| "batch_norm_stats_target": [] | |
| } |