{"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 0x7f2078e4eef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f2078e51840>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685407008938987451, "learning_rate": 0.00096, "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": "[[0.42815977 0.00071948 0.58220965]\n [0.42815977 0.00071948 0.58220965]\n [0.42815977 0.00071948 0.58220965]\n [0.42815977 0.00071948 0.58220965]]", "desired_goal": "[[ 0.14039937 -0.9009783 1.5909294 ]\n [-1.7311257 -1.3802768 -0.27937528]\n [-0.09647524 0.05841625 -1.5605792 ]\n [ 0.31876808 1.6306767 -0.3861418 ]]", "observation": "[[ 4.2815977e-01 7.1947614e-04 5.8220965e-01 6.5928072e-02\n -5.4302637e-04 5.7697620e-02]\n [ 4.2815977e-01 7.1947614e-04 5.8220965e-01 6.5928072e-02\n -5.4302637e-04 5.7697620e-02]\n [ 4.2815977e-01 7.1947614e-04 5.8220965e-01 6.5928072e-02\n -5.4302637e-04 5.7697620e-02]\n [ 4.2815977e-01 7.1947614e-04 5.8220965e-01 6.5928072e-02\n -5.4302637e-04 5.7697620e-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": "[[ 0.10648457 0.0421214 0.23350248]\n [-0.11420194 0.00045099 0.04764068]\n [-0.08770119 0.13464388 0.15197235]\n [ 0.1210637 -0.07846553 0.05517968]]", "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": true, "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": 31250, "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": 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"}} |