a2c-PandaReachDense-v3 / config.json
ftorresa's picture
Initial commit
cde7f33
raw
history blame
14.4 kB
{"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 0x7f38ff19c1f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f38ff18b740>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700840947209944394, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.21792997 0.00214119 0.42925534]\n [ 0.56258273 -0.4112831 0.04572256]\n [ 0.21792997 0.00214119 0.42925534]\n [-1.4172003 -0.8814067 0.6414306 ]]", "desired_goal": "[[ 1.4327445 -1.1635563 0.39214984]\n [ 1.2225146 -0.835383 -0.88812506]\n [-0.05423862 0.98188007 1.2935858 ]\n [-1.2375183 -0.8321382 1.638727 ]]", "observation": "[[ 0.21792997 0.00214119 0.42925534 0.47263187 -0.00334042 0.38091877]\n [ 0.56258273 -0.4112831 0.04572256 0.13777314 -1.6027197 -1.5355697 ]\n [ 0.21792997 0.00214119 0.42925534 0.47263187 -0.00334042 0.38091877]\n [-1.4172003 -0.8814067 0.6414306 -0.78339005 0.11462833 1.4973401 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEAAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.0145703 0.04738529 0.04948742]\n [ 0.1297288 -0.07532164 0.09423502]\n [ 0.06228807 -0.09860373 0.18092349]\n [-0.08138163 -0.06482369 0.09036922]]", "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float32", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.0-89-generic-x86_64-with-glibc2.35 # 99-Ubuntu SMP Mon Oct 30 20:42:41 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a4", "PyTorch": "2.1.1+cu121", "GPU Enabled": "False", "Numpy": "1.23.5", "Gym": "0.28.1"}}