File size: 15,804 Bytes
8a00703 |
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 0x7b34ca624790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b34ca61b680>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVtgAAAAAAAAB9lCiMCG5ldF9hcmNolF2UKEuAS4BljAxsb2dfc3RkX2luaXSUSv7///+MCm9ydGhvX2luaXSUiYwPb3B0aW1pemVyX2NsYXNzlIwTdG9yY2gub3B0aW0ucm1zcHJvcJSMB1JNU3Byb3CUk5SMEG9wdGltaXplcl9rd2FyZ3OUfZQojAVhbHBoYZRHP++uFHrhR66MA2Vwc5RHPuT4tYjjaPGMDHdlaWdodF9kZWNheZRLAHV1Lg==", "net_arch": [128, 128], "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": 1690514614671424869, "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": "[[ 0.27114475 -0.1128292 1.5444462 ]\n [ 0.27114475 -0.1128292 1.5444462 ]\n [ 0.27114475 -0.1128292 1.5444462 ]\n [ 0.27114475 -0.1128292 1.5444462 ]]", "desired_goal": "[[-1.0022203 -0.36252502 0.49100918]\n [ 0.28679192 1.0927312 -0.6850372 ]\n [ 1.6950457 0.23045082 0.5762815 ]\n [ 1.013862 1.6664684 0.06594165]]", "observation": "[[ 2.7114475e-01 -1.1282920e-01 1.5444462e+00 1.6606960e-02\n 1.3388428e-03 6.7319058e-02]\n [ 2.7114475e-01 -1.1282920e-01 1.5444462e+00 1.6606960e-02\n 1.3388428e-03 6.7319058e-02]\n [ 2.7114475e-01 -1.1282920e-01 1.5444462e+00 1.6606960e-02\n 1.3388428e-03 6.7319058e-02]\n [ 2.7114475e-01 -1.1282920e-01 1.5444462e+00 1.6606960e-02\n 1.3388428e-03 6.7319058e-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": "[[-0.04208018 0.05009178 0.24521475]\n [-0.02651003 -0.02717187 0.26301688]\n [ 0.04268821 -0.01418619 0.22094728]\n [ 0.03629336 0.11428391 0.127534 ]]", "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": 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:": "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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"}} |