sac-BipedalWalkerHardcore-v3/data
20.7 KB · 123 lines · plaintext Raw
1 {
2 "policy_class": {
3 ":type:": "<class 'abc.ABCMeta'>",
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5 "__module__": "stable_baselines3.sac.policies",
6 "__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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 ",
7 "__init__": "<function SACPolicy.__init__ at 0x7f9b0477e710>",
8 "_build": "<function SACPolicy._build at 0x7f9b0477e7a0>",
9 "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7f9b0477e830>",
10 "reset_noise": "<function SACPolicy.reset_noise at 0x7f9b0477e8c0>",
11 "make_actor": "<function SACPolicy.make_actor at 0x7f9b0477e950>",
12 "make_critic": "<function SACPolicy.make_critic at 0x7f9b0477e9e0>",
13 "forward": "<function SACPolicy.forward at 0x7f9b0477ea70>",
14 "_predict": "<function SACPolicy._predict at 0x7f9b0477eb00>",
15 "set_training_mode": "<function SACPolicy.set_training_mode at 0x7f9b0477eb90>",
16 "__abstractmethods__": "frozenset()",
17 "_abc_impl": "<_abc_data object at 0x7f9b04760ab0>"
18 },
19 "verbose": 1,
20 "policy_kwargs": {
21 "net_arch": [
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25 "use_sde": false
26 },
27 "observation_space": {
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32 "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]",
33 "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]",
34 "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]",
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39 },
40 "action_space": {
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89 "batch_size": 256,
90 "learning_starts": 10000,
91 "tau": 0.01,
92 "gamma": 0.99,
93 "gradient_steps": 1,
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95 "replay_buffer_class": {
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98 "__module__": "stable_baselines3.common.buffers",
99 "__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:\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 :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 ",
100 "__init__": "<function ReplayBuffer.__init__ at 0x7f9b04be0b90>",
101 "add": "<function ReplayBuffer.add at 0x7f9b04be0c20>",
102 "sample": "<function ReplayBuffer.sample at 0x7f9b047447a0>",
103 "_get_samples": "<function ReplayBuffer._get_samples at 0x7f9b04744830>",
104 "__abstractmethods__": "frozenset()",
105 "_abc_impl": "<_abc_data object at 0x7f9b04c375d0>"
106 },
107 "replay_buffer_kwargs": {},
108 "train_freq": {
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111 },
112 "use_sde_at_warmup": false,
113 "target_entropy": -4.0,
114 "log_ent_coef": null,
115 "ent_coef": 0.005,
116 "target_update_interval": 1,
117 "ent_coef_optimizer": null,
118 "_last_dones": {
119 ":type:": "<class 'numpy.ndarray'>",
120 ":serialized:": "gASViQAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwGFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDAQCUdJRiLg=="
121 },
122 "remove_time_limit_termination": false
123 }