{"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 0x7fdcfdf30310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fdcfdf321c0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "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": 1681854613253806342, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAnpjfPk8ChjwZexA/npjfPk8ChjwZexA/npjfPk8ChjwZexA/npjfPk8ChjwZexA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAox6iP1Mb2T+bq82+elGKv4lURL+93/g+r+y/P+SYX77Weq0/c1tmPwvfk78M99M+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAACemN8+TwKGPBl7ED8eSXw3Bx5TOwCcfjuemN8+TwKGPBl7ED8eSXw3Bx5TOwCcfjuemN8+TwKGPBl7ED8eSXw3Bx5TOwCcfjuemN8+TwKGPBl7ED8eSXw3Bx5TOwCcfjuUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[0.43671125 0.01635852 0.5643783 ]\n [0.43671125 0.01635852 0.5643783 ]\n [0.43671125 0.01635852 0.5643783 ]\n [0.43671125 0.01635852 0.5643783 ]]", "desired_goal": "[[ 1.26656     1.6961464  -0.40169987]\n [-1.0806115  -0.7669149   0.486082  ]\n [ 1.4994105  -0.21835667  1.3553112 ]\n [ 0.8998329  -1.1552442   0.4139942 ]]", "observation": "[[4.3671125e-01 1.6358523e-02 5.6437832e-01 1.5037394e-05 3.2213943e-03\n  3.8850307e-03]\n [4.3671125e-01 1.6358523e-02 5.6437832e-01 1.5037394e-05 3.2213943e-03\n  3.8850307e-03]\n [4.3671125e-01 1.6358523e-02 5.6437832e-01 1.5037394e-05 3.2213943e-03\n  3.8850307e-03]\n [4.3671125e-01 1.6358523e-02 5.6437832e-01 1.5037394e-05 3.2213943e-03\n  3.8850307e-03]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "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": "[[-0.10876963  0.01080947  0.00344155]\n [-0.00342056  0.00391146  0.23584172]\n [-0.03496606 -0.00482556  0.22322132]\n [ 0.08930954 -0.05921574  0.02273494]]", "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": 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:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}