a2c-PandaReachDense-v2 / config.json
Ziyu23's picture
Initial commit
f6981d5
{"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 0x7fd76ec2b370>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fd76ec34740>"}, "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": 1686172425149343703, "learning_rate": 0.001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAusHHPm85grw5GxE/usHHPm85grw5GxE/usHHPm85grw5GxE/usHHPm85grw5GxE/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA2qm1v7iVkD+o7JU+ONYiP7c3hD+oJNM/nSpKPmbvjb6RdUu/icYMv3/Yl7+X2VW/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAC6wcc+bzmCvDkbET9xrJ49aaF0umdmlT26wcc+bzmCvDkbET9xrJ49aaF0umdmlT26wcc+bzmCvDkbET9xrJ49aaF0umdmlT26wcc+bzmCvDkbET9xrJ49aaF0umdmlT2UaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.3901499 -0.01589653 0.56682163]\n [ 0.3901499 -0.01589653 0.56682163]\n [ 0.3901499 -0.01589653 0.56682163]\n [ 0.3901499 -0.01589653 0.56682163]]", "desired_goal": "[[-1.419246 1.129569 0.29282117]\n [ 0.6360812 1.0329503 1.6495562 ]\n [ 0.19742818 -0.2772171 -0.7947627 ]\n [-0.5499044 -1.1862944 -0.8353514 ]]", "observation": "[[ 0.3901499 -0.01589653 0.56682163 0.07747734 -0.00093319 0.07294922]\n [ 0.3901499 -0.01589653 0.56682163 0.07747734 -0.00093319 0.07294922]\n [ 0.3901499 -0.01589653 0.56682163 0.07747734 -0.00093319 0.07294922]\n [ 0.3901499 -0.01589653 0.56682163 0.07747734 -0.00093319 0.07294922]]"}, "_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": "[[ 0.10424551 -0.14295956 0.15375234]\n [ 0.08329178 -0.08411156 0.22780916]\n [ 0.10570466 -0.0339264 0.14754775]\n [ 0.01194126 -0.0492365 0.1488643 ]]", "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": 0.001, "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:": "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"}}