| { | |
| "policy_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
| ":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", | |
| "__module__": "stable_baselines3.sac.policies", | |
| "__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 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 MultiInputPolicy.__init__ at 0x000001BDEC6FF9C0>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x000001BDEC724340>" | |
| }, | |
| "verbose": 1, | |
| "policy_kwargs": { | |
| "use_sde": false | |
| }, | |
| "num_timesteps": 250000, | |
| "_total_timesteps": 500000, | |
| "_num_timesteps_at_start": 0, | |
| "seed": 42, | |
| "action_noise": null, | |
| "start_time": 1755338302920359500, | |
| "learning_rate": 0.001, | |
| "tensorboard_log": "./logs_pnp_sac_her/tb", | |
| "_last_obs": null, | |
| "_last_episode_starts": { | |
| ":type:": "<class 'numpy.ndarray'>", | |
| ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAABlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4=" | |
| }, | |
| "_last_original_obs": { | |
| ":type:": "<class 'collections.OrderedDict'>", | |
| ":serialized:": "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", | |
| "achieved_goal": "[[1.27298779 0.80880931 0.42478449]]", | |
| "desired_goal": "[[1.26958552 0.80704427 0.42469975]]", | |
| "observation": "[[ 1.54009709e+00 7.34624728e-01 5.01351109e-01 1.27298779e+00\n 8.08809311e-01 4.24784489e-01 -2.67109294e-01 7.41845833e-02\n -7.65666195e-02 5.47799135e-03 7.36954931e-03 2.37466429e-10\n -4.51886581e-10 6.67884380e-04 -1.43473194e-02 -3.34605558e-03\n 9.15539159e-03 -1.29406048e-10 2.46253163e-10 1.89982826e-19\n 1.43473194e-02 3.34605559e-03 -9.15539158e-03 -2.14089228e-03\n -2.34595506e-03]]" | |
| }, | |
| "_episode_num": 4999, | |
| "use_sde": false, | |
| "sde_sample_freq": -1, | |
| "_current_progress_remaining": 0.5000020000000001, | |
| "_stats_window_size": 100, | |
| "ep_info_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
| ":serialized:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHwBwAAAAAAACMAWyUSzKMAXSUR0DOJR2sV+I/dX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOJpOluWKNdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOKASpBHCodX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOKXiYLLIQdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOKuQ7muDBdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOLFo08/2TdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOLcQuf29MdX2UKGgGR8AyAAAAAAAAaAdLMmgIR0DOLzUG3WnTdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOMKUAaNuMdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOMhT50r9VdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOM4zHAAQydX2UKGgGRwAAAAAAAAAAaAdLMmgIR0DONQBjH4oJdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DONmtdE9dNdX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DON9roGIKudX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DOOUrhky1vdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOOrkR6F/QdX2UKGgGR8AqAAAAAAAAaAdLMmgIR0DOPCsMqjJudX2UKGgGR8AQAAAAAAAAaAdLMmgIR0DOPZf0wrUcdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOPwWn0kGBdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOQH2hM8HOdX2UKGgGR8AmAAAAAAAAaAdLMmgIR0DOQfWahHskdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DORj6hUR4AdX2UKGgGR8BJAAAAAAAAaAdLMmgIR0DOR6hGjKxLdX2UKGgGR8AmAAAAAAAAaAdLMmgIR0DOSRXh86V/dX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOSoncrRShdX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOTAwiJO32dX2UKGgGR8AwAAAAAAAAaAdLMmgIR0DOTXsT37DVdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOTvENQTEjdX2UKGgGR8AuAAAAAAAAaAdLMmgIR0DOUGcGorFwdX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DOUdoQWepXdX2UKGgGR8AxAAAAAAAAaAdLMmgIR0DOU1PAoG6gdX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOVMbFbVz7dX2UKGgGR8A8AAAAAAAAaAdLMmgIR0DOVjgrjHXFdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOV6glByCGdX2UKGgGR8AmAAAAAAAAaAdLMmgIR0DOWRoeeWfLdX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOWooYBNmEdX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DOXAENc4YKdX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DOXW8CgbqAdX2UKGgGR8AYAAAAAAAAaAdLMmgIR0DOXuj72tdSdX2UKGgGR8AmAAAAAAAAaAdLMmgIR0DOYFj1VYITdX2UKGgGR8AmAAAAAAAAaAdLMmgIR0DOYcTu6VdHdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOYzdWp6yCdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOZLEjFAE/dX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOZh0cp9ZzdX2UKGgGR8AuAAAAAAAAaAdLMmgIR0DOZ5S5TZQIdX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DOaQqyt3fRdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOaoHjfek6dX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOa/Hc+JP7dX2UKGgGR8AmAAAAAAAAaAdLMmgIR0DObWPWattAdX2UKGgGR8AuAAAAAAAAaAdLMmgIR0DObtfP3SKFdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOcFCLCN0edX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOcc2N5t3wdX2UKGgGR8AYAAAAAAAAaAdLMmgIR0DOcz+HWSU1dX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOdLcghbGFdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOdi8Z1mrbdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOd6dHc1wYdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOeSJuGbkPdX2UKGgGR8A0AAAAAAAAaAdLMmgIR0DOepf05EMLdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOfA3uTibVdX2UKGgGR8AqAAAAAAAAaAdLMmgIR0DOfY3ndO6/dX2UKGgGR8AYAAAAAAAAaAdLMmgIR0DOfv/g75mAdX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOg0/8jzI4dX2UKGgGR8AQAAAAAAAAaAdLMmgIR0DOhMGvMbFTdX2UKGgGR8AYAAAAAAAAaAdLMmgIR0DOhjmokzGhdX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOh6miBXjmdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOiSSWX1J2dX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOip6Pp6hQdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOjBiJAMUidX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOjYuHYYixdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOjwU9wFTvdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOkHI/7iyZdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOkev1lGwzdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOk2Xu3MINdX2UKGgGR8AzAAAAAAAAaAdLMmgIR0DOlNnoTwlTdX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOlk3hwVCYdX2UKGgGR8AuAAAAAAAAaAdLMmgIR0DOl8rULDyfdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOmTyD5CWvdX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOmrR9PUKBdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOnCp2ll9SdX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOnaRv5xiodX2UKGgGR8AyAAAAAAAAaAdLMmgIR0DOnxVu1ndwdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOoImclPaddX2UKGgGR8AwAAAAAAAAaAdLMmgIR0DOoflJlJ6IdX2UKGgGR8AUAAAAAAAAaAdLMmgIR0DOo2zg88s+dX2UKGgGR8AwAAAAAAAAaAdLMmgIR0DOpOLaVUuMdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOplnO0LMLdX2UKGgGRwAAAAAAAAAAaAdLMmgIR0DOp8nIU8FIdX2UKGgGR8AiAAAAAAAAaAdLMmgIR0DOqT3DBMzudX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOqrO7HyVfdX2UKGgGR8AgAAAAAAAAaAdLMmgIR0DOrCFe6ZpjdX2UKGgGR8AsAAAAAAAAaAdLMmgIR0DOrZL1bqyGdX2UKGgGRwAAAAAAAAAAaAdLMmgIR0DOrv/4qPOqdX2UKGgGR8AwAAAAAAAAaAdLMmgIR0DOsH2HFglXdX2UKGgGR8AkAAAAAAAAaAdLMmgIR0DOsfOAd4mkdX2UKGgGR8AoAAAAAAAAaAdLMmgIR0DOs2WvhZQpdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOtNmo73fydX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOtkoEdNnHdX2UKGgGR8AyAAAAAAAAaAdLMmgIR0DOt73/HYHxdX2UKGgGR8AcAAAAAAAAaAdLMmgIR0DOuSv4qPOqdX2UKGgGR8AyAAAAAAAAaAdLMmgIR0DOupnzSThYdWUu" | |
| }, | |
| "ep_success_buffer": { | |
| ":type:": "<class 'collections.deque'>", | |
| ":serialized:": "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" | |
| }, | |
| "_n_updates": 749799, | |
| "observation_space": { | |
| ":type:": "<class 'gymnasium.spaces.dict.Dict'>", | |
| ":serialized:": "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", | |
| "spaces": "{'achieved_goal': Box(-inf, inf, (3,), float64), 'desired_goal': Box(-inf, inf, (3,), float64), 'observation': Box(-inf, inf, (25,), float64)}", | |
| "_shape": null, | |
| "dtype": null, | |
| "_np_random": null | |
| }, | |
| "action_space": { | |
| ":type:": "<class 'gymnasium.spaces.box.Box'>", | |
| ":serialized:": "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", | |
| "dtype": "float32", | |
| "_shape": [ | |
| 4 | |
| ], | |
| "low": "[-1. -1. -1. -1.]", | |
| "bounded_below": "[ True True True True]", | |
| "high": "[1. 1. 1. 1.]", | |
| "bounded_above": "[ True True True True]", | |
| "low_repr": "-1.0", | |
| "high_repr": "1.0", | |
| "_np_random": "Generator(PCG64)" | |
| }, | |
| "n_envs": 1, | |
| "buffer_size": 1000000, | |
| "batch_size": 512, | |
| "learning_starts": 100, | |
| "tau": 0.05, | |
| "gamma": 0.95, | |
| "gradient_steps": 1, | |
| "optimize_memory_usage": false, | |
| "replay_buffer_class": { | |
| ":type:": "<class 'abc.ABCMeta'>", | |
| ":serialized:": "gAWVPwAAAAAAAACMJ3N0YWJsZV9iYXNlbGluZXMzLmhlci5oZXJfcmVwbGF5X2J1ZmZlcpSMD0hlclJlcGxheUJ1ZmZlcpSTlC4=", | |
| "__module__": "stable_baselines3.her.her_replay_buffer", | |
| "__annotations__": "{'env': typing.Optional[stable_baselines3.common.vec_env.base_vec_env.VecEnv]}", | |
| "__doc__": "\n Hindsight Experience Replay (HER) buffer.\n Paper: https://arxiv.org/abs/1707.01495\n\n Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n\n .. note::\n\n Compared to other implementations, the ``future`` goal sampling strategy is inclusive:\n the current transition can be used when re-sampling.\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 env: The training environment\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 :param n_sampled_goal: Number of virtual transitions to create per real transition,\n by sampling new goals.\n :param goal_selection_strategy: Strategy for sampling goals for replay.\n One of ['episode', 'final', 'future']\n :param copy_info_dict: Whether to copy the info dictionary and pass it to\n ``compute_reward()`` method.\n Please note that the copy may cause a slowdown.\n False by default.\n ", | |
| "__init__": "<function HerReplayBuffer.__init__ at 0x000001BDEC6FC720>", | |
| "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x000001BDEC6FC7C0>", | |
| "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x000001BDEC6FC860>", | |
| "set_env": "<function HerReplayBuffer.set_env at 0x000001BDEC6FC900>", | |
| "add": "<function HerReplayBuffer.add at 0x000001BDEC6FCA40>", | |
| "_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x000001BDEC6FCAE0>", | |
| "sample": "<function HerReplayBuffer.sample at 0x000001BDEC6FCB80>", | |
| "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x000001BDEC6FCC20>", | |
| "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x000001BDEC6FCCC0>", | |
| "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x000001BDEC6FCD60>", | |
| "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x000001BDEC6FCE00>", | |
| "__abstractmethods__": "frozenset()", | |
| "_abc_impl": "<_abc._abc_data object at 0x000001BDEC7085C0>" | |
| }, | |
| "replay_buffer_kwargs": { | |
| "n_sampled_goal": 4, | |
| "goal_selection_strategy": "future" | |
| }, | |
| "n_steps": 1, | |
| "train_freq": { | |
| ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", | |
| ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu" | |
| }, | |
| "use_sde_at_warmup": false, | |
| "target_entropy": -4.0, | |
| "ent_coef": "auto", | |
| "target_update_interval": 1, | |
| "lr_schedule": { | |
| ":type:": "<class 'stable_baselines3.common.utils.FloatSchedule'>", | |
| ":serialized:": "gAWVeQAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMDUZsb2F0U2NoZWR1bGWUk5QpgZR9lIwOdmFsdWVfc2NoZWR1bGWUaACMEENvbnN0YW50U2NoZWR1bGWUk5QpgZR9lIwDdmFslEc/UGJN0vGp/HNic2Iu", | |
| "value_schedule": "ConstantSchedule(val=0.001)" | |
| }, | |
| "batch_norm_stats": [], | |
| "batch_norm_stats_target": [] | |
| } |