{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\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 full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` 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 squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7fe70d3a8af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe70d3a4f40>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1684853834955236614, "learning_rate": 0.0002, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.324576   0.02609585 0.48550266]\n [0.324576   0.02609585 0.48550266]\n [0.324576   0.02609585 0.48550266]\n [0.324576   0.02609585 0.48550266]]", "desired_goal": "[[ 1.7217575  -0.9381302   0.11890852]\n [-0.04031397 -1.5546912   0.47196493]\n [-0.45732215 -1.6520525  -0.9508673 ]\n [-0.49139476 -0.61912507  1.5026172 ]]", "observation": "[[0.324576   0.02609585 0.48550266 0.03488075 0.00603568 0.02225335]\n [0.324576   0.02609585 0.48550266 0.03488075 0.00603568 0.02225335]\n [0.324576   0.02609585 0.48550266 0.03488075 0.00603568 0.02225335]\n [0.324576   0.02609585 0.48550266 0.03488075 0.00603568 0.02225335]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAabYAPvlkeb1azpQ+q6NmPe4S7j2mXcs9gcQWPsTOn70C19k9z5BkvUM83z3Mjoc+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.12569584 -0.06088731  0.29063684]\n [ 0.05630843  0.11624704  0.09929971]\n [ 0.14723398 -0.07803109  0.10636713]\n [-0.05580216  0.10900166  0.26476133]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": true, "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:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}