| {"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 0x7f11f9025a20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f11f9021600>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVAwEAAAAAAAB9lCiMDWFjdGl2YXRpb25fZm6UjBt0b3JjaC5ubi5tb2R1bGVzLmFjdGl2YXRpb26UjARUYW5olJOUjAhuZXRfYXJjaJR9lCiMAnBplF2UKEtAS0BljAJ2ZpRdlChLQEtAZXWMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "activation_fn": "<class 'torch.nn.modules.activation.Tanh'>", "net_arch": {"pi": [64, 64], "vf": [64, 64]}, "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": 0, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688415280436778790, "learning_rate": {":type:": "<class 'function'>", ":serialized:": "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"}, "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": "[[-1.3088329 -0.9005011 0.10883743]\n [ 0.07866688 1.3453434 -0.61469394]\n [-1.5428628 0.8186574 1.4453648 ]\n [-1.0268254 -1.4452307 -0.79686564]\n [ 1.1042383 0.27865815 -1.1576539 ]\n [ 0.23936437 0.65548027 0.44711 ]\n [-1.4734269 -1.130269 -1.1146122 ]\n [-1.4623632 -1.3849425 1.2006778 ]\n [ 1.3929636 -1.3305675 0.9028658 ]\n [ 0.6844924 0.47028247 1.0440718 ]\n [-1.3908352 -1.0472386 -1.101998 ]\n [ 1.208916 -1.1899908 -0.7408243 ]\n [-0.93034863 0.21976903 1.0987828 ]\n [-0.13704324 0.4305036 -0.80074686]\n [-0.8886525 1.1504261 -0.10649846]\n [ 1.3060198 -0.6382353 1.4885994 ]]", "desired_goal": "[[-1.1877666 -0.9217639 0.15658985]\n [ 0.14366823 1.2722042 -0.68709856]\n [-1.6599275 0.8243215 1.6984466 ]\n [-0.9221194 -1.4154884 -0.82110107]\n [ 1.1236908 0.26249802 -1.2460166 ]\n [ 0.2766943 0.6422118 0.4531395 ]\n [-1.5415252 -1.3195676 -1.4170587 ]\n [-1.5917088 -1.210294 1.1806313 ]\n [ 1.4320735 -1.3743551 0.89857537]\n [ 0.6817695 0.43737566 1.0600533 ]\n [-1.7016059 -1.1657679 -1.6870794 ]\n [ 1.2666656 -1.2544936 -0.73701406]\n [-0.8327307 0.23636685 1.1232054 ]\n [-0.15759364 0.4209854 -0.93918276]\n [-0.87726414 1.1830753 0.0069183 ]\n [ 1.2855582 -0.6953371 1.4797539 ]]", "observation": "[[-1.3088329 -0.9005011 0.10883743 -1.6644406 0.48163706 0.16049293]\n [ 0.07866688 1.3453434 -0.61469394 0.13303721 -1.5174274 -0.2598568 ]\n [-1.5428628 0.8186574 1.4453648 -1.0242573 -0.13587192 3.2644181 ]\n [-1.0268254 -1.4452307 -0.79686564 -1.6238836 0.43661365 -2.5471394 ]\n [ 1.1042383 0.27865815 -1.1576539 -0.4043254 0.04003777 -2.1328545 ]\n [ 0.23936437 0.65548027 0.44711 0.0210629 0.35060412 -0.04173031]\n [-1.4734269 -1.130269 -1.1146122 -4.0613394 0.4913565 -1.3792695 ]\n [-1.4623632 -1.3849425 1.2006778 -1.2562187 2.377117 -0.10392059]\n [ 1.3929636 -1.3305675 0.9028658 0.6925787 1.0475515 -0.0700907 ]\n [ 0.6844924 0.47028247 1.0440718 0.18011297 0.42805755 0.07086583]\n [-1.3908352 -1.0472386 -1.101998 -3.184468 0.00952675 -1.2341074 ]\n [ 1.208916 -1.1899908 -0.7408243 0.22843114 -0.24043848 -2.023788 ]\n [-0.93034863 0.21976903 1.0987828 -0.35340914 -0.19238243 0.13039412]\n [-0.13704324 0.4305036 -0.80074686 0.19121912 0.24043131 0.11862841]\n [-0.8886525 1.1504261 -0.10649846 1.9793293 -0.695861 2.9820971 ]\n [ 1.3060198 -0.6382353 1.4885994 0.47546795 0.94620967 0.9083634 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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]\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]\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]\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]\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": "[[ 0.00361382 -0.08299267 0.2724363 ]\n [-0.0248854 0.13611135 0.23491007]\n [-0.04953203 0.10749163 0.01480229]\n [ 0.06142563 -0.05133546 0.04918363]\n [ 0.11155985 -0.09803017 0.1928825 ]\n [-0.04738491 0.05069221 0.2376346 ]\n [-0.02809466 -0.09725989 0.16767499]\n [ 0.07587653 -0.08375536 0.26048726]\n [-0.14387925 -0.02650775 0.03618746]\n [ 0.08457874 -0.1178415 0.27914652]\n [ 0.04398051 -0.11462587 0.0684261 ]\n [-0.07651556 -0.07624921 0.00944746]\n [ 0.10153716 -0.13283238 0.24203831]\n [-0.12858996 -0.11618862 0.27132004]\n [ 0.08612169 -0.08464515 0.18367212]\n [-0.01927285 0.08128588 0.18565269]]", "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]\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]\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]\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]\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": -6.4000000000064e-05, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 19580, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "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": 16, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}} |