{"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 0x7f1d5c7f1510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f1d5c7f5a80>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1704557191679809129, "learning_rate": 0.0005, "tensorboard_log": "./logs/sac-100k-HER/", "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWV+wMAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolsAAAAAAAAAAd9FoPzMmkL9b04a+j+ydPlrCQT+5Jhq/umyaP+27Y7+G2ye/AVkhP2onpr9G5Sa+aEeNPTtXJcDZSii/3ok0P1WtVj/tbh2/J0O0PtdH+r53USu/BXSCP8w1x79a9yO/YjRcvTK5bT7hGw6/lG8PP6ZSrj5bRya/E/7EPnkc8j4jOKU+r0tFvls2wD6PWBa/tZG7Ps4QZ7/GPf6+wko6P6ROd7/vCuu+wRGAPx8unr+V1Sa/lNlbvXqSCb9TuWE/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcolsAAAAAAAAAAHeYOv9j1q75Hq2g/EksgP1Ii0z+Nol4/cOPjPgnKh7+KenK/EUAVvxADfL+Uavg+AnvVv8Mapr/L6pG/w7hcPoBP0z8SS4s/bRJQvjutsr8HMDi/RrmPPhgwjb8l15k/XCK5P9551z+w8Jy/MiaRv6CnTT9meB2+Iyr5PgkynT8igay/+8egvrt6zD+UiBi/Tp6jv36YR7+bewM+0u2CPR2EDj9zmKc/O7Zdv/84m7+ynAo/6w9cv1H9pz6Vlc6+lGgOSxBLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWgAEAAAAAAAB30Wg/MyaQv1vThr7fNIg+IUyYP78AhD+P7J0+WsJBP7kmGr9X7Li/BSOzv1x/sL+6bJo/7btjv4bbJ7/4GKg+xXsvvu2D0D0BWSE/aiemv0blJr4KN78/ZWmWPmD+0T9oR409O1clwNlKKL/BLb8+apBAPnFsxz3eiTQ/Va1WP+1uHb+Losq8CWHbP0enCT8nQ7Q+10f6vndRK7/9onE/My4uvxM98D0FdII/zDXHv1r3I78fZ28+HPuSPliIiT1iNFy9MrltPuEbDr8BIKQ9/v6UvfB4rz+Ubw8/plKuPltHJr9lvAo+etk4vzlw1z0T/sQ+eRzyPiM4pT5EP1q/HugGQD/her+vS0W+WzbAPo9YFr9whj2+VADJP7ubQL61kbs+zhBnv8Y9/r6x9NM/vxn/vh/Buz/CSjo/pE53v+8K674tYb6+58iRPzAV6L/BEYA/Hy6ev5XVJr88rbI9T+y7vyLZwj2U2Vu9epIJv1O5YT/Jvya+MExXv1dwH8CUaA5LEEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.9094462  -1.1261657  -0.26333126]\n [ 0.30844542  0.7568718  -0.60215336]\n [ 1.2064431  -0.88958627 -0.6556934 ]\n [ 0.63026434 -1.2980778  -0.16298398]\n [ 0.06898385 -2.5834491  -0.6573921 ]\n [ 0.7052287   0.83858234 -0.61497384]\n [ 0.35207483 -0.48882934 -0.6692118 ]\n [ 1.0191656  -1.5563293  -0.64049304]\n [-0.05376089  0.23215178 -0.5551129 ]\n [ 0.5602963   0.3404743  -0.6495263 ]\n [ 0.38475093  0.47287348  0.3226939 ]\n [-0.19267152  0.3754147  -0.5872888 ]\n [ 0.36634603 -0.90260017 -0.49656504]\n [ 0.7277032  -0.9660437  -0.4590678 ]\n [ 1.0005418  -1.2357825  -0.6516965 ]\n [-0.0536743  -0.5373913   0.8817341 ]]", "desired_goal": "[[-0.55819875 -0.33586     0.9088635 ]\n [ 0.6261455   1.6494849   0.8696678 ]\n [ 0.4450946  -1.0608531  -0.9471823 ]\n [-0.5830088  -0.98442173  0.48518813]\n [-1.6678164  -1.2976917  -1.1399778 ]\n [ 0.21554856  1.6508636   1.0882285 ]\n [-0.20319529 -1.3959116  -0.71948284]\n [ 0.2807104  -1.1030302   1.2018782 ]\n [ 1.4463611   1.6834066  -1.2260952 ]\n [-1.1339781   0.803339   -0.1537796 ]\n [ 0.4866496   1.2280895  -1.3476908 ]\n [-0.31402573  1.5974954  -0.595834  ]\n [-1.2782686  -0.7796706   0.1284012 ]\n [ 0.06393017  0.5567034   1.3093399 ]\n [-0.86606187 -1.2126769   0.5414535 ]\n [-0.8596179   0.32810453 -0.40348497]]", "observation": "[[ 0.9094462  -1.1261657  -0.26333126  0.26602837  1.1898233   1.0312728 ]\n [ 0.30844542  0.7568718  -0.60215336 -1.4447125  -1.3995062  -1.3788867 ]\n [ 1.2064431  -0.88958627 -0.6556934   0.3283155  -0.17137058  0.10181413]\n [ 0.63026434 -1.2980778  -0.16298398  1.4938672   0.29377285  1.6405754 ]\n [ 0.06898385 -2.5834491  -0.6573921   0.37339595  0.1880509   0.0973748 ]\n [ 0.7052287   0.83858234 -0.61497384 -0.02473571  1.7138988   0.5377087 ]\n [ 0.35207483 -0.48882934 -0.6692118   0.94389325 -0.68039244  0.11730399]\n [ 1.0191656  -1.5563293  -0.64049304  0.23379181  0.28707206  0.06715459]\n [-0.05376089  0.23215178 -0.5551129   0.08013917 -0.07275198  1.3708782 ]\n [ 0.5602963   0.3404743  -0.6495263   0.1354843  -0.7220684   0.10519452]\n [ 0.38475093  0.47287348  0.3226939  -0.85252786  2.1079173  -0.9799995 ]\n [-0.19267152  0.3754147  -0.5872888  -0.18508315  1.5703225  -0.18809406]\n [ 0.36634603 -0.90260017 -0.49656504  1.6559049  -0.4982433   1.4668311 ]\n [ 0.7277032  -0.9660437  -0.4590678  -0.37183514  1.1389436  -1.8131466 ]\n [ 1.0005418  -1.2357825  -0.6516965   0.08724448 -1.4681491   0.09514071]\n [-0.0536743  -0.5373913   0.8817341  -0.16284098 -0.8410063  -2.4912317 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.12185754 -0.12923501  0.04026817]\n [ 0.05590134  0.12882273  0.00984115]\n [ 0.15445116 -0.09681337  0.00503312]\n [ 0.09121902 -0.1527944   0.04927959]\n [ 0.02962187 -0.32894596  0.00488058]\n [ 0.0994459   0.14002061  0.00868983]\n [ 0.0606894  -0.04189231  0.00381914]\n [ 0.13389859 -0.18818605  0.00639815]\n [ 0.01615138  0.05691335  0.01406549]\n [ 0.08354045  0.07175823  0.00558694]\n [ 0.06427541  0.08990265  0.09289461]\n [ 0.00090678  0.07654657  0.01117602]\n [ 0.06225558 -0.09859683  0.01932323]\n [ 0.10191235 -0.10729135  0.02269057]\n [ 0.13185474 -0.14425725  0.00539205]\n [ 0.01616088 -0.0485474   0.14309773]]", "desired_goal": "[[-0.05262057 -0.03138294  0.2309426 ]\n [ 0.05086377  0.14378574  0.22754005]\n [ 0.0350441  -0.09534971  0.06981981]\n [-0.0547884  -0.0886061   0.19416347]\n [-0.14957555 -0.11624618  0.05308328]\n [ 0.01498708  0.1439074   0.24651323]\n [-0.02160146 -0.1249122   0.08958633]\n [ 0.02068072 -0.09907103  0.25637913]\n [ 0.12253168  0.14677869  0.04560746]\n [-0.1029304   0.06912956  0.13869487]\n [ 0.03867505  0.10660566  0.03505178]\n [-0.03128549  0.13919868  0.10032025]\n [-0.11553805 -0.07054073  0.1631909 ]\n [ 0.00173913  0.04736869  0.26570785]\n [-0.0795207  -0.10874525  0.19904785]\n [-0.07895765  0.02719922  0.11701801]]", "observation": "[[ 1.2185754e-01 -1.2923501e-01  4.0268168e-02  5.5410471e-02\n   5.8704334e-01  4.1171518e-01]\n [ 5.5901337e-02  1.2882273e-01  9.8411459e-03 -5.2919376e-01\n  -6.7318815e-01 -7.0479822e-01]\n [ 1.5445116e-01 -9.6813366e-02  5.0331214e-03  7.6695576e-02\n  -7.5452253e-02 -1.8859264e-02]\n [ 9.1219023e-02 -1.5279441e-01  4.9279589e-02  4.7499463e-01\n   1.5093392e-01  6.9397640e-01]\n [ 2.9621875e-02 -3.2894596e-01  4.8805787e-03  9.2100732e-02\n   9.9478848e-02 -2.0915793e-02]\n [ 9.9445902e-02  1.4002061e-01  8.6898338e-03 -4.3951113e-02\n   8.4211189e-01  1.8307017e-01]\n [ 6.0689401e-02 -4.1892305e-02  3.8191411e-03  2.8705436e-01\n  -3.2319415e-01 -1.1683540e-02]\n [ 1.3389859e-01 -1.8818605e-01  6.3981544e-03  4.4394404e-02\n   1.4767264e-01 -3.4915399e-02]\n [ 1.6151380e-02  5.6913346e-02  1.4065492e-02 -8.1126643e-03\n  -2.7454400e-02  5.6903839e-01]\n [ 8.3540447e-02  7.1758226e-02  5.5869441e-03  1.0800192e-02\n  -3.4347793e-01 -1.7293289e-02]\n [ 6.4275406e-02  8.9902647e-02  9.2894606e-02 -3.2682902e-01\n   1.0338814e+00 -5.2001256e-01]\n [ 9.0677704e-04  7.6546572e-02  1.1176019e-02 -9.8745964e-02\n   7.7223307e-01 -1.5316008e-01]\n [ 6.2255580e-02 -9.8596834e-02  1.9323226e-02  5.3036708e-01\n  -2.3454182e-01  6.1348879e-01]\n [ 1.0191235e-01 -1.0729135e-01  2.2690566e-02 -1.6256393e-01\n   5.6228012e-01 -9.0597039e-01]\n [ 1.3185474e-01 -1.4425725e-01  5.3920536e-03 -5.6845970e-03\n  -7.0659679e-01 -2.1950744e-02]\n [ 1.6160883e-02 -4.8547402e-02  1.4309773e-01 -9.1145240e-02\n  -4.0136522e-01 -1.2200953e+00]]"}, "_episode_num": 2179, "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:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKImJiYmJiYmJiYiJiYmJiYmJiYiJiImJiYmIiImJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmJiYmIiYmJiYiJiYmJiYmJiYmJiYmJiIiJiYmJiIllLg=="}, "_n_updates": 6188, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": "Generator(PCG64)"}, "n_envs": 16, "buffer_size": 10000, "batch_size": 256, "learning_starts": 1000, "tau": 0.005, "gamma": 0.99, "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 0x7f1d5c7da200>", "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x7f1d5c7da290>", "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x7f1d5c7da320>", "set_env": "<function HerReplayBuffer.set_env at 0x7f1d5c7da3b0>", "add": "<function HerReplayBuffer.add at 0x7f1d5c7da440>", "_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x7f1d5c7da4d0>", "sample": "<function HerReplayBuffer.sample at 0x7f1d5c7da560>", "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x7f1d5c7da5f0>", "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x7f1d5c7da680>", "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x7f1d5c7da710>", "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x7f1d5c7da7a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f1d5c7e21c0>"}, "replay_buffer_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVggAAAAAAAAB9lCiMDm5fc2FtcGxlZF9nb2FslEsEjBdnb2FsX3NlbGVjdGlvbl9zdHJhdGVneZSMLXN0YWJsZV9iYXNlbGluZXMzLmhlci5nb2FsX3NlbGVjdGlvbl9zdHJhdGVneZSMFUdvYWxTZWxlY3Rpb25TdHJhdGVneZSTlEsAhZRSlHUu", "n_sampled_goal": 4, "goal_selection_strategy": "GoalSelectionStrategy.FUTURE"}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -3.0, "ent_coef": "auto", "target_update_interval": 1, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Thu Oct 5 21:02:42 UTC 2023", "Python": "3.10.13", "Stable-Baselines3": "2.2.1", "PyTorch": "2.1.0", "GPU Enabled": "False", "Numpy": "1.26.3", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1"}}