a2c-PandaReachDense-v3 / config.json
tylerkiser's picture
Initial commit
147a75b
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 0x7e34141eb520>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e34141f4a80>"}, "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": 1696289883103027751, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.076364 0.45428047 -0.19676639]\n [-1.5966353 0.41108608 -2.26286 ]\n [ 0.2820409 -0.00471769 0.43296498]\n [ 0.2820409 -0.00471769 0.43296498]]", "desired_goal": "[[-1.4784218 1.2573378 -0.8384432 ]\n [-0.32888943 0.59662586 -1.2812482 ]\n [ 1.1152353 1.448845 0.2284908 ]\n [-1.1916002 -1.5264678 0.0376789 ]]", "observation": "[[-0.076364 0.45428047 -0.19676639 -1.8712438 1.6670791 -1.3896501 ]\n [-1.5966353 0.41108608 -2.26286 -1.5812194 -0.9382393 -0.98561627]\n [ 0.2820409 -0.00471769 0.43296498 0.4725746 -0.00393184 0.38721862]\n [ 0.2820409 -0.00471769 0.43296498 0.4725746 -0.00393184 0.38721862]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.05539054 0.02409642 0.07635114]\n [ 0.09960931 0.05220499 0.08654288]\n [-0.05320001 -0.0272251 0.05784189]\n [-0.12901956 -0.05859916 0.21914901]]", "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:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 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"}}