a2c-PandaReachDense-v3 / config.json
yaystevek's picture
Initial commit
72f0c1e
raw
history blame contribute delete
No virus
14.3 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 0x79c5c849d630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79c5c849acc0>"}, "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": 1693517981281845179, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.16917571 0.05737113 0.50023997]\n [0.01105266 0.23820533 0.23696268]\n [0.16917571 0.05737113 0.50023997]\n [0.0176025 0.2779769 0.23102862]]", "desired_goal": "[[ 1.6798519 -0.06380009 0.35980412]\n [-0.4505443 0.20446517 -0.80728024]\n [-1.5337169 1.6313039 1.510275 ]\n [-0.10313679 0.90335387 -1.1165144 ]]", "observation": "[[ 0.16917571 0.05737113 0.50023997 0.2649847 0.01196092 0.24770886]\n [ 0.01105266 0.23820533 0.23696268 -2.0068128 0.6591385 -1.4591904 ]\n [ 0.16917571 0.05737113 0.50023997 0.2649847 0.01196092 0.24770886]\n [ 0.0176025 0.2779769 0.23102862 -2.0623202 1.6975298 -1.4874352 ]]"}, "_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": "[[ 0.07504659 0.02995064 0.04370062]\n [-0.01016022 -0.06005395 0.15775943]\n [-0.11146627 0.12179367 0.17233261]\n [ 0.10035059 0.14640188 0.2800111 ]]", "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:": "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", "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuDQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}