File size: 15,573 Bytes
d0dd1b8
1
{"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 0x7f226676e440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f22667782c0>"}, "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": 1686041850899472076, "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": "[[0.42137665 0.0161056  0.57407427]\n [0.42137665 0.0161056  0.57407427]\n [0.42137665 0.0161056  0.57407427]\n [0.42137665 0.0161056  0.57407427]]", "desired_goal": "[[-1.6660756   0.6944156   0.18673722]\n [ 0.77989453 -1.0049138   1.0680424 ]\n [ 1.0049088   0.5530975  -1.2369913 ]\n [-0.933022   -1.0741082  -1.5389464 ]]", "observation": "[[ 0.42137665  0.0161056   0.57407427  0.00124578 -0.00199943  0.00525149]\n [ 0.42137665  0.0161056   0.57407427  0.00124578 -0.00199943  0.00525149]\n [ 0.42137665  0.0161056   0.57407427  0.00124578 -0.00199943  0.00525149]\n [ 0.42137665  0.0161056   0.57407427  0.00124578 -0.00199943  0.00525149]]"}, "_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////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAV9dfPYf0kb0wfWk+C5mNvenzLz2QaS8+zUkTvRem7L3ZxIQ+SdVVPA4tF73ZKII+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": "[[ 0.05464872 -0.07126718  0.22801661]\n [-0.06913956  0.04295722  0.17130113]\n [-0.03595905 -0.11555117  0.25931433]\n [ 0.01305134 -0.0369082   0.2542179 ]]", "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI9P3UeOnm9r+UhpRSlIwBbJRLMowBdJRHQKhEdBUrCnB1fZQoaAZoCWgPQwhGCI82jtj+v5SGlFKUaBVLMmgWR0CoRDtYjjaPdX2UKGgGaAloD0MIOltAaD388r+UhpRSlGgVSzJoFkdAqEP9Drqt5nV9lChoBmgJaA9DCBPx1vm3i/S/lIaUUpRoFUsyaBZHQKhDxCWNWEN1fZQoaAZoCWgPQwhPPj22ZcD/v5SGlFKUaBVLMmgWR0CoRYK02LpBdX2UKGgGaAloD0MIE9cxrri467+UhpRSlGgVSzJoFkdAqEVJ7XxvvXV9lChoBmgJaA9DCO8fC9Eh8O2/lIaUUpRoFUsyaBZHQKhFC1TBInV1fZQoaAZoCWgPQwgEjgQabOr1v5SGlFKUaBVLMmgWR0CoRNJG4I8hdX2UKGgGaAloD0MIlUc3wqIi5b+UhpRSlGgVSzJoFkdAqEaYRqXWv3V9lChoBmgJaA9DCKG9+njoe/G/lIaUUpRoFUsyaBZHQKhGX3JPqLV1fZQoaAZoCWgPQwiSyhRzEHTqv5SGlFKUaBVLMmgWR0CoRiDCHh0hdX2UKGgGaAloD0MIKNap8j2j5b+UhpRSlGgVSzJoFkdAqEXns1KoRHV9lChoBmgJaA9DCHcP0H05s+e/lIaUUpRoFUsyaBZHQKhHpld1Mdt1fZQoaAZoCWgPQwjyRBDn4QTtv5SGlFKUaBVLMmgWR0CoR25Gz8gqdX2UKGgGaAloD0MIhuKON/kt57+UhpRSlGgVSzJoFkdAqEcvtF8XvnV9lChoBmgJaA9DCMjNcAM+v+q/lIaUUpRoFUsyaBZHQKhG9r1M/Ql1fZQoaAZoCWgPQwjWqIdodIfsv5SGlFKUaBVLMmgWR0CoSLqMFUyYdX2UKGgGaAloD0MI/0KPGD339b+UhpRSlGgVSzJoFkdAqEiBu2qkunV9lChoBmgJaA9DCINsWb4uQ+W/lIaUUpRoFUsyaBZHQKhIQw8nuzB1fZQoaAZoCWgPQwjkLVc/Nsnlv5SGlFKUaBVLMmgWR0CoSAoBq9GrdX2UKGgGaAloD0MIx5xn7Eu26b+UhpRSlGgVSzJoFkdAqEnQKBun/HV9lChoBmgJaA9DCPj6Wpca4fW/lIaUUpRoFUsyaBZHQKhJl1aGHpN1fZQoaAZoCWgPQwiFl+DUBxLpv5SGlFKUaBVLMmgWR0CoSViswL3LdX2UKGgGaAloD0MI7xr0pbe/87+UhpRSlGgVSzJoFkdAqEkffqHGj3V9lChoBmgJaA9DCGzOwTOhqQnAlIaUUpRoFUsyaBZHQKhK6QSSNfh1fZQoaAZoCWgPQwiiKTv9oK7tv5SGlFKUaBVLMmgWR0CoSrA/cFhYdX2UKGgGaAloD0MIv5oDBHP0AcCUhpRSlGgVSzJoFkdAqEpxiI+GGnV9lChoBmgJaA9DCK7TSEvlbfO/lIaUUpRoFUsyaBZHQKhKOHZ9NN91fZQoaAZoCWgPQwhqvko+dvcCwJSGlFKUaBVLMmgWR0CoS/7CJoCddX2UKGgGaAloD0MIh272B8rt/7+UhpRSlGgVSzJoFkdAqEvF9hJAdHV9lChoBmgJaA9DCHZwsDcxJOW/lIaUUpRoFUsyaBZHQKhLh2IwdsB1fZQoaAZoCWgPQwh4eqUsQ1wAwJSGlFKUaBVLMmgWR0CoS05s9B8hdX2UKGgGaAloD0MId4TTghd997+UhpRSlGgVSzJoFkdAqE0iOcUdrHV9lChoBmgJaA9DCKWjHMwmwO+/lIaUUpRoFUsyaBZHQKhM6Yht+Ct1fZQoaAZoCWgPQwjhs3VwsHf3v5SGlFKUaBVLMmgWR0CoTKrmp2lmdX2UKGgGaAloD0MIJJnVO9wOAcCUhpRSlGgVSzJoFkdAqExx5iVjZ3V9lChoBmgJaA9DCDXs98Q6lfO/lIaUUpRoFUsyaBZHQKhOOAIY3vR1fZQoaAZoCWgPQwhmpN5TOa33v5SGlFKUaBVLMmgWR0CoTf8x9G7SdX2UKGgGaAloD0MIvVKWIY718L+UhpRSlGgVSzJoFkdAqE3Aj8k2P3V9lChoBmgJaA9DCItPATCegfe/lIaUUpRoFUsyaBZHQKhNh3EAHVx1fZQoaAZoCWgPQwjpfHiWIKMGwJSGlFKUaBVLMmgWR0CoT0f+KjzqdX2UKGgGaAloD0MIrDqrBfaY7b+UhpRSlGgVSzJoFkdAqE8PPRiPQ3V9lChoBmgJaA9DCBJNoIhFDPG/lIaUUpRoFUsyaBZHQKhO0JWNm191fZQoaAZoCWgPQwiNRj6veGrzv5SGlFKUaBVLMmgWR0CoTpeD3/PxdX2UKGgGaAloD0MIIVor2hyn8r+UhpRSlGgVSzJoFkdAqFBIhdMTOHV9lChoBmgJaA9DCHhEherm4vy/lIaUUpRoFUsyaBZHQKhQD4Oc2BJ1fZQoaAZoCWgPQwgzjSYXYwAIwJSGlFKUaBVLMmgWR0CoT9DXe3x4dX2UKGgGaAloD0MI+wYmN4rs/L+UhpRSlGgVSzJoFkdAqE+Xkili0HV9lChoBmgJaA9DCI/ecB+5df2/lIaUUpRoFUsyaBZHQKhRWUHpr1x1fZQoaAZoCWgPQwiEgefew+UDwJSGlFKUaBVLMmgWR0CoUSBe5WildX2UKGgGaAloD0MIkfKTap+uAMCUhpRSlGgVSzJoFkdAqFDhu4wyqXV9lChoBmgJaA9DCF0ZVBuciPi/lIaUUpRoFUsyaBZHQKhQqLGaQV91fZQoaAZoCWgPQwjnilJCsOrwv5SGlFKUaBVLMmgWR0CoUsJ4KQaKdX2UKGgGaAloD0MImODUB5K387+UhpRSlGgVSzJoFkdAqFKK4H5aeXV9lChoBmgJaA9DCIVdFD3wcfm/lIaUUpRoFUsyaBZHQKhSTSm65G11fZQoaAZoCWgPQwj6Yu/FF23wv5SGlFKUaBVLMmgWR0CoUhTV2A5JdX2UKGgGaAloD0MItftVgO/WAsCUhpRSlGgVSzJoFkdAqFR0/pt78nV9lChoBmgJaA9DCH4dOGdEafe/lIaUUpRoFUsyaBZHQKhUPLi++M91fZQoaAZoCWgPQwgUd7zJb5H/v5SGlFKUaBVLMmgWR0CoU/6l1r6+dX2UKGgGaAloD0MIPLznwHKE8L+UhpRSlGgVSzJoFkdAqFPGRoysS3V9lChoBmgJaA9DCNodUgyQqPq/lIaUUpRoFUsyaBZHQKhWL0zTF2p1fZQoaAZoCWgPQwjMY83IIHfrv5SGlFKUaBVLMmgWR0CoVfdFnZkDdX2UKGgGaAloD0MIcsEZ/P3i77+UhpRSlGgVSzJoFkdAqFW5ksjFAHV9lChoBmgJaA9DCKt7ZHPVvOa/lIaUUpRoFUsyaBZHQKhVgU47zTZ1fZQoaAZoCWgPQwj2YFJ8fELnv5SGlFKUaBVLMmgWR0CoWAbI1cdHdX2UKGgGaAloD0MIDJBoAkWs7r+UhpRSlGgVSzJoFkdAqFfOrKeTV3V9lChoBmgJaA9DCGnIeJRKeOy/lIaUUpRoFUsyaBZHQKhXkQZGax51fZQoaAZoCWgPQwivRKD6BxEGwJSGlFKUaBVLMmgWR0CoV1ktdzGQdX2UKGgGaAloD0MIZFkw8UfR7r+UhpRSlGgVSzJoFkdAqFn77bcoIHV9lChoBmgJaA9DCNALdy6MdPe/lIaUUpRoFUsyaBZHQKhZxCHARCh1fZQoaAZoCWgPQwiA8KFES97+v5SGlFKUaBVLMmgWR0CoWYZUcXFcdX2UKGgGaAloD0MI9pZyvti79L+UhpRSlGgVSzJoFkdAqFlOVkc0cnV9lChoBmgJaA9DCLpJDAIrh/S/lIaUUpRoFUsyaBZHQKhb5hzeXRh1fZQoaAZoCWgPQwhTA83n3K33v5SGlFKUaBVLMmgWR0CoW638O09hdX2UKGgGaAloD0MI8UqS5/q+BsCUhpRSlGgVSzJoFkdAqFtwMUh3aHV9lChoBmgJaA9DCKHbSxqjdfe/lIaUUpRoFUsyaBZHQKhbN6TGHYZ1fZQoaAZoCWgPQwgi/IugMdPxv5SGlFKUaBVLMmgWR0CoXb7bUPQOdX2UKGgGaAloD0MIAVEwYwp2BcCUhpRSlGgVSzJoFkdAqF2GCyyD7XV9lChoBmgJaA9DCFb0h2ae/ArAlIaUUpRoFUsyaBZHQKhdRyup0fZ1fZQoaAZoCWgPQwjWOJuOAC7yv5SGlFKUaBVLMmgWR0CoXQ36yjYadX2UKGgGaAloD0MIzXaFPljGAMCUhpRSlGgVSzJoFkdAqF7KbONYKnV9lChoBmgJaA9DCGKHMenv5fm/lIaUUpRoFUsyaBZHQKhekYOUdJd1fZQoaAZoCWgPQwhDkIMSZpr5v5SGlFKUaBVLMmgWR0CoXlLHMlkZdX2UKGgGaAloD0MIZ3v0hvvI/r+UhpRSlGgVSzJoFkdAqF4ZqmCROnV9lChoBmgJaA9DCOKsiJroc/C/lIaUUpRoFUsyaBZHQKhfylpGnXN1fZQoaAZoCWgPQwgAVkeOdEbzv5SGlFKUaBVLMmgWR0CoX5FfJFLGdX2UKGgGaAloD0MIPrK5ap5j87+UhpRSlGgVSzJoFkdAqF9SaCtihHV9lChoBmgJaA9DCDF9ryE4LgrAlIaUUpRoFUsyaBZHQKhfGUs4DLd1fZQoaAZoCWgPQwiXytsRTsvyv5SGlFKUaBVLMmgWR0CoYMsUAT7EdX2UKGgGaAloD0MIM1TFVPrJ9b+UhpRSlGgVSzJoFkdAqGCR93KSxXV9lChoBmgJaA9DCEM50a5CSvu/lIaUUpRoFUsyaBZHQKhgU0bcXWR1fZQoaAZoCWgPQwjFcHUAxF34v5SGlFKUaBVLMmgWR0CoYBocBEKFdX2UKGgGaAloD0MI7WXbaWvE+L+UhpRSlGgVSzJoFkdAqGHMuSOinHV9lChoBmgJaA9DCExPWOIBpf6/lIaUUpRoFUsyaBZHQKhhk/CZWq91fZQoaAZoCWgPQwhZwW9DjNfxv5SGlFKUaBVLMmgWR0CoYVVAZ88cdX2UKGgGaAloD0MIs7PonQo4CMCUhpRSlGgVSzJoFkdAqGEb6JqIrXV9lChoBmgJaA9DCMDOTZtxWvm/lIaUUpRoFUsyaBZHQKhixhLoOhF1fZQoaAZoCWgPQwiSPq2iP3Txv5SGlFKUaBVLMmgWR0CoYoz5O8CgdX2UKGgGaAloD0MIETXR56MM9b+UhpRSlGgVSzJoFkdAqGJOHJtBOnV9lChoBmgJaA9DCKa6gJcZtvu/lIaUUpRoFUsyaBZHQKhiFO/tY0V1ZS4="}, "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:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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"}}