{"policy_class": {":type:": "", ":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__": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eeb8d7c7000>"}, "verbose": 1, "policy_kwargs": {"use_sde": false}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717177351936896473, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "", ":serialized:": "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", "achieved_goal": "[[ 3.5310036e-01 -4.8691934e-04 4.7642648e-01]\n [ 7.1426541e-01 1.2370547e+00 -9.0125614e-01]\n [-1.4570868e+00 2.3601387e+00 -1.3871872e+00]\n [-8.7548882e-02 4.8807681e-01 5.5790949e-01]]", "desired_goal": "[[-0.0097986 -0.7409015 -1.4519526 ]\n [ 0.73397094 1.0916963 -1.4993796 ]\n [-1.0518646 1.535729 -1.1591806 ]\n [-0.50272113 1.411682 1.6405178 ]]", "observation": "[[ 3.5310036e-01 -4.8691934e-04 4.7642648e-01 5.1625788e-01\n -2.6369570e-03 3.6056933e-01]\n [ 7.1426541e-01 1.2370547e+00 -9.0125614e-01 1.1112688e-01\n 7.8178418e-01 -1.7256832e+00]\n [-1.4570868e+00 2.3601387e+00 -1.3871872e+00 -1.9253107e+00\n 1.0690117e+00 -1.0111072e+00]\n [-8.7548882e-02 4.8807681e-01 5.5790949e-01 2.3916928e-01\n 1.5933988e+00 1.2122046e+00]]"}, "_last_episode_starts": {":type:": "", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 5.4846156e-02 5.9490740e-02 1.1913483e-01]\n [-4.3790918e-02 1.1347931e-01 9.1529377e-02]\n [ 1.8422486e-02 2.3486093e-02 2.0202914e-01]]", "desired_goal": "[[-0.00714243 -0.06712595 0.01103506]\n [ 0.06075642 0.09775589 0.00671766]\n [-0.10227283 0.13770625 0.0376868 ]\n [-0.05214142 0.12654553 0.29255018]]", "observation": "[[ 3.84396687e-02 -2.19447225e-12 1.97400138e-01 0.00000000e+00\n -0.00000000e+00 0.00000000e+00]\n [ 5.48461564e-02 5.94907403e-02 1.19134828e-01 -1.48055211e-01\n 4.50520337e-01 -1.30306494e+00]\n [-4.37909178e-02 1.13479309e-01 9.15293768e-02 -8.92271757e-01\n 6.15485132e-01 -8.56743634e-01]\n [ 1.84224863e-02 2.34860927e-02 2.02029139e-01 -1.01262085e-01\n 9.16658878e-01 5.31928003e-01]]"}, "_episode_num": 302360, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "", ":serialized:": "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"}, "ep_success_buffer": {":type:": "", ":serialized:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlLg=="}, "_n_updates": 249975, "buffer_size": 1000000, "batch_size": 256, "learning_starts": 100, "tau": 0.005, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "", ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observation_space': , 'obs_shape': typing.Dict[str, typing.Tuple[int, ...]], 'observations': typing.Dict[str, numpy.ndarray], 'next_observations': typing.Dict[str, numpy.ndarray]}", "__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__": "", "add": "", "sample": "", "_get_samples": "", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eeb8d9d1240>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -3.0, "ent_coef": "auto", "target_update_interval": 1, "observation_space": {":type:": "", ":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:": "", ":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": 4, "lr_schedule": {":type:": "", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.3.2", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}