{ "policy_class": { ":type:": "", ":serialized:": "gAWVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=", "__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 ", "__init__": "", "_build": "", "_get_constructor_parameters": "", "reset_noise": "", "make_actor": "", "make_critic": "", "forward": "", "_predict": "", "set_training_mode": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7efcf0f28200>" }, "verbose": 1, "policy_kwargs": { "use_sde": false }, "observation_space": { ":type:": "", ":serialized:": "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", "dtype": "float64", "_shape": [ 17 ], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False]", "_np_random": null }, "action_space": { ":type:": "", ":serialized:": "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", "dtype": "float32", "_shape": [ 6 ], "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True True True True True True]", "bounded_above": "[ True True True True True True]", "_np_random": "RandomState(MT19937)" }, "n_envs": 1, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": 0, "action_noise": null, "start_time": 1676160389507497688, "learning_rate": 0.0003, "tensorboard_log": "runs/HalfCheetah-v3__tqc__602331063__1676160385/HalfCheetah-v3", "lr_schedule": { ":type:": "", ":serialized:": "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" }, "_last_obs": null, "_last_episode_starts": { ":type:": "", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg==" }, "_last_original_obs": { ":type:": "", ":serialized:": "gAWV/QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaIAAAAAAAAALjUxysjBae/lWZ4HbZlub991cjS6cjSP/qgqbPjW7M/n2XN1ktRwj/nodr83t7wvywBNVuh97q/g4Si/FB4yr82JhTTV6AnQC/a/5KSY+E/+GhcWe9m9j+sJfFDX8gxQAte7LQIzhfAJ6UhzrJuKECATyJuF+rNvxibKm9alQLAzKwCg+Iy7r+UjAVudW1weZSMBWR0eXBllJOUjAJmOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwFLEYaUjAFDlHSUUpQu" }, "_episode_num": 1000, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": { ":type:": "", ":serialized:": "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" }, "ep_success_buffer": { ":type:": "", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg==" }, "_n_updates": 990000, "buffer_size": 1, "batch_size": 256, "learning_starts": 10000, "tau": 0.005, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": { ":type:": "", ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", "__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__": "", "add": "", "sample": "", "_get_samples": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7efcf13a8580>" }, "replay_buffer_kwargs": {}, "train_freq": { ":type:": "", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu" }, "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": [] }