esperesa's picture
Initial commit
06f2b5b verified
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMQAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "sb3_contrib.tqc.policies", "__doc__": "\n Policy class (with both actor and critic) for TQC.\n\n :param observation_space: Observation space\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n :param features_extractor_class: Features extractor to use.\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 :param n_quantiles: Number of quantiles for the critic.\n :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function MultiInputPolicy.__init__ at 0x715a28a5b0a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x715a28a57d00>"}, "verbose": 1, "policy_kwargs": {"net_arch": [512, 512, 512], "n_critics": 2, "use_sde": false}, "num_timesteps": 10000000, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1726855976903048469, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": null, "_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": "[[ 0.03820468 -0.083819 0.01998943]\n [ 0.00089974 -0.09956303 0.02066902]\n [ 0.02100242 -0.12776668 0.02 ]\n [-0.01934359 -0.14985053 0.01998953]]", "desired_goal": "[[ 0.00717665 -0.03560627 0.17415717]\n [ 0.03948959 0.13494505 0.21545143]\n [ 0.11905613 0.12129459 0.08058682]\n [ 0.04721569 -0.08107396 0.15043007]]", "observation": "[[ 5.97301051e-02 -7.01609775e-02 1.20660737e-01 -2.31865555e-01\n -6.11204267e-01 -1.34299219e+00 1.48691370e-08 3.82046774e-02\n -8.38190019e-02 1.99894346e-02 4.48783976e-06 -3.55768534e-05\n -7.74457658e-06 -1.61770095e-06 1.09538014e-05 -9.57692350e-07\n 9.50089607e-09 -4.78768852e-05 -9.66187727e-05]\n [ 4.40273574e-03 -8.11988190e-02 4.97105950e-03 -9.94576886e-03\n 1.08760826e-01 -1.30629197e-01 7.97775388e-02 8.99739796e-04\n -9.95630324e-02 2.06690244e-02 3.45551446e-02 6.86109634e-05\n -5.35912476e-02 -3.55481356e-03 2.87076980e-01 6.55809045e-02\n 3.39154601e+00 -1.59704641e-01 -6.60929918e+00]\n [ 3.84396687e-02 -2.19447225e-12 1.97400138e-01 0.00000000e+00\n -0.00000000e+00 0.00000000e+00 0.00000000e+00 2.10024156e-02\n -1.27766684e-01 1.99999996e-02 0.00000000e+00 -0.00000000e+00\n 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n [ 2.21672226e-02 -2.47940477e-02 1.66382670e-01 -8.18376303e-01\n -1.23073614e+00 -1.04348004e+00 1.69341234e-08 -1.93435904e-02\n -1.49850532e-01 1.99895296e-02 4.48686751e-06 -3.08392118e-05\n -3.87872387e-06 -5.73867237e-06 1.09538505e-05 -5.08076300e-06\n 5.10964995e-08 -2.53998151e-04 -9.67000524e-05]]"}, "_episode_num": 1178047, "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:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlLg=="}, "_n_updates": 2499750, "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, (19,), 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 True]", "bounded_above": "[ True True True True]", "_shape": [4], "low": "[-1. -1. -1. -1.]", "high": "[1. 1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": "Generator(PCG64)"}, "n_envs": 4, "buffer_size": 1000000, "batch_size": 2048, "learning_starts": 1000, "tau": 0.05, "gamma": 0.95, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVPwAAAAAAAACMJ3N0YWJsZV9iYXNlbGluZXMzLmhlci5oZXJfcmVwbGF5X2J1ZmZlcpSMD0hlclJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.her.her_replay_buffer", "__annotations__": "{'env': typing.Optional[stable_baselines3.common.vec_env.base_vec_env.VecEnv]}", "__doc__": "\n Hindsight Experience Replay (HER) buffer.\n Paper: https://arxiv.org/abs/1707.01495\n\n Replay buffer for sampling HER (Hindsight Experience Replay) transitions.\n\n .. note::\n\n Compared to other implementations, the ``future`` goal sampling strategy is inclusive:\n the current transition can be used when re-sampling.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param env: The training environment\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n :param n_sampled_goal: Number of virtual transitions to create per real transition,\n by sampling new goals.\n :param goal_selection_strategy: Strategy for sampling goals for replay.\n One of ['episode', 'final', 'future']\n :param copy_info_dict: Whether to copy the info dictionary and pass it to\n ``compute_reward()`` method.\n Please note that the copy may cause a slowdown.\n False by default.\n ", "__init__": "<function HerReplayBuffer.__init__ at 0x715a77fe5e10>", "__getstate__": "<function HerReplayBuffer.__getstate__ at 0x715a77fe5ea0>", "__setstate__": "<function HerReplayBuffer.__setstate__ at 0x715a77fe5f30>", "set_env": "<function HerReplayBuffer.set_env at 0x715a77fe5fc0>", "add": "<function HerReplayBuffer.add at 0x715a77fe6050>", "_compute_episode_length": "<function HerReplayBuffer._compute_episode_length at 0x715a77fe60e0>", "sample": "<function HerReplayBuffer.sample at 0x715a77fe6170>", "_get_real_samples": "<function HerReplayBuffer._get_real_samples at 0x715a77fe6200>", "_get_virtual_samples": "<function HerReplayBuffer._get_virtual_samples at 0x715a77fe6290>", "_sample_goals": "<function HerReplayBuffer._sample_goals at 0x715a77fe6320>", "truncate_last_trajectory": "<function HerReplayBuffer.truncate_last_trajectory at 0x715a77fe63b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x715a77ff8740>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -4.0, "ent_coef": "auto", "target_update_interval": 1, "top_quantiles_to_drop_per_net": 2, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.8.0-41-generic-x86_64-with-glibc2.39 # 41-Ubuntu SMP PREEMPT_DYNAMIC Fri Aug 2 20:41:06 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.3.2", "PyTorch": "2.4.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.0.0", "Gymnasium": "0.29.1", "OpenAI Gym": "0.26.2"}}