{"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 0x7b526dfc2200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b526dfbe580>"}, "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": 1689775721556975119, "learning_rate": 0.0007, "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": "[[ 0.44148278 -0.01419787  0.55792415]\n [ 0.44148278 -0.01419787  0.55792415]\n [ 0.44148278 -0.01419787  0.55792415]\n [ 0.44148278 -0.01419787  0.55792415]]", "desired_goal": "[[-0.17190994 -1.502647   -0.867908  ]\n [ 0.31585324  1.2973197   1.4392196 ]\n [-1.0124656   0.42585906  0.26015654]\n [ 0.4239725   0.18421243  1.350358  ]]", "observation": "[[ 0.44148278 -0.01419787  0.55792415 -0.01459876 -0.00324087 -0.017144  ]\n [ 0.44148278 -0.01419787  0.55792415 -0.01459876 -0.00324087 -0.017144  ]\n [ 0.44148278 -0.01419787  0.55792415 -0.01459876 -0.00324087 -0.017144  ]\n [ 0.44148278 -0.01419787  0.55792415 -0.01459876 -0.00324087 -0.017144  ]]"}, "_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": "[[-0.1100338   0.01501395  0.16539042]\n [ 0.05446376 -0.13928083  0.13255264]\n [ 0.09238146  0.12255495  0.05345511]\n [-0.067747    0.11243147  0.16975304]]", "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-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}