Emperor-WS's picture
Initial commit
dc9d8ab
{
"policy_class": {
":type:": "<class 'abc.ABCMeta'>",
":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=",
"__module__": "stable_baselines3.sac.policies",
"__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
"__doc__": "\n Policy class (with both actor and critic) for SAC.\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 features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\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_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 SACPolicy.__init__ at 0x7fa5c6bd2200>",
"_build": "<function SACPolicy._build at 0x7fa5c6bd2290>",
"_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fa5c6bd2320>",
"reset_noise": "<function SACPolicy.reset_noise at 0x7fa5c6bd23b0>",
"make_actor": "<function SACPolicy.make_actor at 0x7fa5c6bd2440>",
"make_critic": "<function SACPolicy.make_critic at 0x7fa5c6bd24d0>",
"forward": "<function SACPolicy.forward at 0x7fa5c6bd2560>",
"_predict": "<function SACPolicy._predict at 0x7fa5c6bd25f0>",
"set_training_mode": "<function SACPolicy.set_training_mode at 0x7fa5c6bd2680>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7fa5c6bd7840>"
},
"verbose": 1,
"policy_kwargs": {
"log_std_init": -3,
"net_arch": [
400,
300
],
"use_sde": true
},
"num_timesteps": 1000000,
"_total_timesteps": 1000000,
"_num_timesteps_at_start": 0,
"seed": 0,
"action_noise": null,
"start_time": 1671835216851530424,
"learning_rate": {
":type:": "<class 'function'>",
":serialized:": "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"
},
"tensorboard_log": "runs/AntBulletEnv-v0__sac__3073263478__1671835214/AntBulletEnv-v0",
"_last_obs": null,
"_last_episode_starts": {
":type:": "<class 'numpy.ndarray'>",
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAAGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
},
"_last_original_obs": {
":type:": "<class 'numpy.ndarray'>",
":serialized:": "gAWV5QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZwAAAAAAAAADs1ar7Q0ni994Z/P2+7DD+Jpx++bUctvsi5vDy/8AW+dxc4PztWHL/vWTO9/nagPjUdgL+7/ba6EzTgPi88UT0QBUG/Eho2v+/aYD+3ZwU/QkWWvjO7wL613Ha/JJ5kPgAAgD8AAAAAAACAPwAAAACUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwFLHIaUjAFDlHSUUpQu"
},
"_episode_num": 1019,
"use_sde": true,
"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": 990000,
"observation_space": {
":type:": "<class 'gymnasium.spaces.box.Box'>",
":serialized:": "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",
"dtype": "float32",
"bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
"bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
"_shape": [
28
],
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
"high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
"low_repr": "-inf",
"high_repr": "inf",
"_np_random": null
},
"action_space": {
":type:": "<class 'gymnasium.spaces.box.Box'>",
":serialized:": "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",
"dtype": "float32",
"bounded_below": "[ True True True True True True True True]",
"bounded_above": "[ True True True True True True True True]",
"_shape": [
8
],
"low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
"high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
"low_repr": "-1.0",
"high_repr": "1.0",
"_np_random": "Generator(PCG64)"
},
"n_envs": 1,
"buffer_size": 1,
"batch_size": 256,
"learning_starts": 10000,
"tau": 0.02,
"gamma": 0.98,
"gradient_steps": 8,
"optimize_memory_usage": false,
"replay_buffer_class": {
":type:": "<class 'abc.ABCMeta'>",
":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
"__module__": "stable_baselines3.common.buffers",
"__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}",
"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\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 device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\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 ",
"__init__": "<function ReplayBuffer.__init__ at 0x7fa5c6b06440>",
"add": "<function ReplayBuffer.add at 0x7fa5c6b064d0>",
"sample": "<function ReplayBuffer.sample at 0x7fa5c6b06560>",
"_get_samples": "<function ReplayBuffer._get_samples at 0x7fa5c6b065f0>",
"_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7fa5c6b06680>)>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc._abc_data object at 0x7fa59b5364c0>"
},
"replay_buffer_kwargs": {},
"train_freq": {
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLCGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
},
"use_sde_at_warmup": false,
"target_entropy": -8.0,
"ent_coef": "auto",
"target_update_interval": 1,
"lr_schedule": {
":type:": "<class 'function'>",
":serialized:": "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"
},
"_action_repeat": [
null
],
"surgeon": null,
"batch_norm_stats": [],
"batch_norm_stats_target": [],
"_last_action": {
":type:": "<class 'numpy.ndarray'>",
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHVHdL8NsXe/8pp3v8Avb7+1xnq/GFl1v4o8br90Eno/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
}
}