| {"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "stable_baselines3.sac.policies", "__firstlineno__": 437, "__doc__": "\nPolicy 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 0x79de76806480>", "__static_attributes__": [], "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79de768205c0>"}, "verbose": 1, "policy_kwargs": {"net_arch": [400, 300], "use_sde": false}, "num_timesteps": 500000, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1791831111803822748, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVeAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWBAAAAAAAAAABAQEBlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksEhZSMAUOUdJRSlC4="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVjAIAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwTbnVtcHkuX2NvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYwAAAAAAAAAL/kjz1PGXm9CtejPCQukbx5X3W9k8GjPLKtvb2Lscy91sCjPJfP0T0t7Re+0L+jPJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLBEsDhpSMAUOUdJRSlIwMZGVzaXJlZF9nb2FslGgHKJYwAAAAAAAAAAshB7wjEOM9k58yPbuDkz07mwW+g91CPr8th72jkkq9EXCVPa/EET52zd+953slPpRoDksESwOGlGgSdJRSlIwLb2JzZXJ2YXRpb26UaAcoljABAAAAAAAA6nIdPRlsGqxDI0o+AAAAAAAAAIAAAAAAAAAAAL/kjz1PGXm9CtejPAAAAAAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACSr6j3bO64+AjGGO6M2H70tFuE9drq8OxC5oz0kLpG8eV91vZPBozxYxp43JRirNvYvETl7H+C3oJMvONF3qLcufPa6cGCcusgWDzlauyc+8QUfvUMbjjtnnyE9u9WPvfLJTLoqq4w9sq29vYuxzL3WwKM8yZiWNjfWGbj9++K4dS8xtcrCNzdQjoysTB+TLqpziq9SsMq4LG46PpfhIj5R7Aw+ruK6PGXJgT1YgYW8szNANJfP0T0t7Re+0L+jPDjYjDhMOhs3OvxvuC6yqbev7he2EIlYrATjUi+CUVoulH37uJRoDksESxOGlGgSdJRSlHUu", "achieved_goal": "[[ 0.07026052 -0.06081515 0.02 ]\n [-0.0177222 -0.0599055 0.01998976]\n [-0.09261645 -0.09994801 0.01998941]\n [ 0.10244673 -0.14836569 0.01998892]]", "desired_goal": "[[-0.00824762 0.11087062 0.04360921]\n [ 0.0720286 -0.13047497 0.19029813]\n [-0.06600522 -0.04945625 0.07296766]\n [ 0.14235185 -0.1092786 0.16160546]]", "observation": "[[ 3.84396687e-02 -2.19447225e-12 1.97400138e-01 0.00000000e+00\n -0.00000000e+00 0.00000000e+00 0.00000000e+00 7.02605173e-02\n -6.08151518e-02 1.99999996e-02 0.00000000e+00 -0.00000000e+00\n 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n [ 1.14584237e-01 3.40300411e-01 4.09519766e-03 -3.88704650e-02\n 1.09905578e-01 5.75953256e-03 7.99428225e-02 -1.77221969e-02\n -5.99055029e-02 1.99897643e-02 1.89274288e-05 5.09900792e-06\n 1.38461444e-04 -2.67175401e-05 4.18607378e-05 -2.00829545e-05\n -1.88053190e-03 -1.19305961e-03 1.36460294e-04]\n [ 1.63800627e-01 -3.88240255e-02 4.33674594e-03 3.94586585e-02\n -7.02318773e-02 -7.81207462e-04 6.86858445e-02 -9.26164538e-02\n -9.99480113e-02 1.99894123e-02 4.48813489e-06 -3.66775457e-05\n -1.08234563e-04 -6.60066974e-07 1.09530029e-05 -3.99483918e-12\n 6.69034550e-11 -2.51842380e-10 -9.66495281e-05]\n [ 1.82060897e-01 1.59063682e-01 1.37620226e-01 2.28131674e-02\n 6.33724108e-02 -1.62970275e-02 1.79002015e-07 1.02446727e-01\n -1.48365691e-01 1.99889243e-02 6.71599410e-05 9.25229324e-06\n -5.72169447e-05 -2.02293413e-05 -2.26396855e-06 -3.07715312e-12\n 1.91800409e-10 4.96398547e-11 -1.19920034e-04]]"}, "_episode_num": 12185, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKImJiYiJiYmJiYmIiYiJiYmJiImJiYiJiYiIiImIiYmIiYmJiYiJiImJiImJiYmJiYiJiYmJiIiJiYmIiYmJiYmJiYiIiYmIiYmIiYmIiYmJiImIiYmJiYiIiYmIiImJiYmJiIhlLg=="}, "_n_updates": 490000, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "{'achieved_goal': Box(-10.0, 10.0, (3,), float32), 'desired_goal': Box(-10.0, 10.0, (3,), float32), 'observation': Box(-10.0, 10.0, (19,), float32)}", "_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": null, "high_repr": null, "_np_random": "Generator(PCG64)"}, "n_envs": 1, "buffer_size": 200000, "batch_size": 512, "learning_starts": 10000, "tau": 0.05, "gamma": 0.95, "gradient_steps": 4, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVPwAAAAAAAACMJ3N0YWJsZV9iYXNlbGluZXMzLmhlci5oZXJfcmVwbGF5X2J1ZmZlcpSMD0hlclJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.her.her_replay_buffer", "__firstlineno__": 15, "__annotations__": "{'env': stable_baselines3.common.vec_env.base_vec_env.VecEnv | None}", "__doc__": "\nHindsight Experience Replay (HER) buffer.\nPaper: https://arxiv.org/abs/1707.01495\n\nReplay 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 0x79de769fac00>", "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x79de769faca0>", "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x79de769fad40>", "set_env": "<function HerReplayBuffer.set_env at 0x79de769fade0>", "add": "<function HerReplayBuffer.add at 0x79de769faf20>", "_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x79de769fafc0>", "sample": "<function HerReplayBuffer.sample at 0x79de769fb060>", "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x79de769fb100>", "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x79de769fb1a0>", "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x79de769fb240>", "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x79de769fb2e0>", "__static_attributes__": ["_current_ep_start", "copy_info_dict", "env", "ep_length", "ep_start", "goal_selection_strategy", "her_ratio", "infos", "n_sampled_goal"], "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79de7694d840>"}, "replay_buffer_kwargs": {"n_sampled_goal": 8, "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/M6kqMFUyYXNic2Iu", "value_schedule": "ConstantSchedule(val=0.0003)"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.6.122+-x86_64-with-glibc2.39 # 1 SMP Thu Apr 30 18:17:14 UTC 2026", "Python": "3.13.15", "Stable-Baselines3": "2.9.0", "PyTorch": "2.11.0+cu130", "GPU Enabled": "True", "Numpy": "2.1.3", "Cloudpickle": "3.1.2", "Gymnasium": "1.3.0", "OpenAI Gym": "0.25.2"}} |