{"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 0x7f11c2122950>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f11c21281c0>"}, "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": 1685799963651799478, "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": "[[4.3961081e-01 4.3405264e-04 5.3710628e-01]\n [4.3961081e-01 4.3405264e-04 5.3710628e-01]\n [4.3961081e-01 4.3405264e-04 5.3710628e-01]\n [4.3961081e-01 4.3405264e-04 5.3710628e-01]]", "desired_goal": "[[-0.47433242 0.6384369 1.012391 ]\n [-0.51635325 -0.19628142 0.77335113]\n [ 0.5548827 -1.0211806 -0.10855568]\n [-0.5743911 -1.3309137 0.72643363]]", "observation": "[[ 4.3961081e-01 4.3405264e-04 5.3710628e-01 9.2903383e-02\n -5.4331409e-04 7.2405025e-02]\n [ 4.3961081e-01 4.3405264e-04 5.3710628e-01 9.2903383e-02\n -5.4331409e-04 7.2405025e-02]\n [ 4.3961081e-01 4.3405264e-04 5.3710628e-01 9.2903383e-02\n -5.4331409e-04 7.2405025e-02]\n [ 4.3961081e-01 4.3405264e-04 5.3710628e-01 9.2903383e-02\n -5.4331409e-04 7.2405025e-02]]"}, "_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////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAmnDjPbOpiD3gffI9jHVnvc0tJb0gYfY90NrBPAp+LD1opjI+6tdaPcNlJrxTLwk+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": "[[ 0.11105461 0.06672993 0.11840415]\n [-0.05650859 -0.04032688 0.12030244]\n [ 0.02366391 0.04211239 0.17446291]\n [ 0.05342857 -0.0101561 0.13396959]]", "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": 0.96, "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"}} |