{"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 0x7f909ac5ee60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f909ac64d40>"}, "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": 1684316159043922155, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.4570061  -0.01435027  0.56818885]\n [ 0.4570061  -0.01435027  0.56818885]\n [ 0.4570061  -0.01435027  0.56818885]\n [ 0.4570061  -0.01435027  0.56818885]]", "desired_goal": "[[-0.65737647  1.0637374  -1.059962  ]\n [-1.4033341  -1.0345775  -1.4278969 ]\n [-1.48274     0.52380806  0.64081323]\n [ 1.1892033  -1.6095204   1.1925557 ]]", "observation": "[[ 4.5700610e-01 -1.4350274e-02  5.6818885e-01  1.2473858e-02\n  -5.1226385e-04  1.4966309e-02]\n [ 4.5700610e-01 -1.4350274e-02  5.6818885e-01  1.2473858e-02\n  -5.1226385e-04  1.4966309e-02]\n [ 4.5700610e-01 -1.4350274e-02  5.6818885e-01  1.2473858e-02\n  -5.1226385e-04  1.4966309e-02]\n [ 4.5700610e-01 -1.4350274e-02  5.6818885e-01  1.2473858e-02\n  -5.1226385e-04  1.4966309e-02]]"}, "_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": "[[ 0.04935899 -0.04652516  0.20606667]\n [ 0.12035722  0.14605667  0.25702915]\n [ 0.02878128  0.08887739  0.00758997]\n [-0.14635383 -0.06400376  0.07164404]]", "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-5.15.107+-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": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}