File size: 15,579 Bytes
fd8f785
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 0x7ff9c2a46440>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff9c2a48440>"}, "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": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690641892544384978, "learning_rate": 0.001, "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": "[[ 0.40630656 -0.00115999  0.581178  ]\n [ 0.40630656 -0.00115999  0.581178  ]\n [ 0.40630656 -0.00115999  0.581178  ]\n [ 0.40630656 -0.00115999  0.581178  ]]", "desired_goal": "[[-1.3359728   1.5252982   1.5165149 ]\n [-0.48453206  1.1402699  -0.60228235]\n [-1.1027447   0.73871356 -1.1684128 ]\n [ 0.7023494   1.6404289  -0.3209404 ]]", "observation": "[[ 0.40630656 -0.00115999  0.581178   -0.01222767 -0.00334125  0.00955946]\n [ 0.40630656 -0.00115999  0.581178   -0.01222767 -0.00334125  0.00955946]\n [ 0.40630656 -0.00115999  0.581178   -0.01222767 -0.00334125  0.00955946]\n [ 0.40630656 -0.00115999  0.581178   -0.01222767 -0.00334125  0.00955946]]"}, "_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": "[[ 0.0275079   0.14099556  0.03018341]\n [-0.11360495 -0.09973964  0.19132128]\n [ 0.03679998  0.05031491  0.09678616]\n [ 0.00081576  0.11109748  0.19654433]]", "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": 5000, "n_steps": 5, "gamma": 0.95, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}