File size: 18,432 Bytes
f3db9c8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
{
"policy_class": {
":type:": "<class 'abc.ABCMeta'>",
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
"__module__": "stable_baselines3.common.policies",
"__doc__": "\n 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\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 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()`` 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: 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 ",
"__init__": "<function ActorCriticPolicy.__init__ at 0x7f6baaf83820>",
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f6baaf838b0>",
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f6baaf83940>",
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f6baaf839d0>",
"_build": "<function ActorCriticPolicy._build at 0x7f6baaf83a60>",
"forward": "<function ActorCriticPolicy.forward at 0x7f6baaf83af0>",
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f6baaf83b80>",
"_predict": "<function ActorCriticPolicy._predict at 0x7f6baaf83c10>",
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f6baaf83ca0>",
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f6baaf83d30>",
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f6baaf83dc0>",
"__abstractmethods__": "frozenset()",
"_abc_impl": "<_abc_data object at 0x7f6baaf822a0>"
},
"verbose": 1,
"policy_kwargs": {},
"observation_space": {
":type:": "<class 'gym.spaces.box.Box'>",
":serialized:": "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",
"dtype": "float32",
"_shape": [
8
],
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
"high": "[inf inf inf inf inf inf inf inf]",
"bounded_below": "[False False False False False False False False]",
"bounded_above": "[False False False False False False False False]",
"_np_random": null
},
"action_space": {
":type:": "<class 'gym.spaces.discrete.Discrete'>",
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
"n": 4,
"_shape": [],
"dtype": "int64",
"_np_random": null
},
"n_envs": 100,
"num_timesteps": 2048000,
"_total_timesteps": 2000000,
"_num_timesteps_at_start": 0,
"seed": null,
"action_noise": null,
"start_time": 1670453338420921249,
"learning_rate": 0.0003,
"tensorboard_log": null,
"lr_schedule": {
":type:": "<class 'function'>",
":serialized:": "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"
},
"_last_obs": {
":type:": "<class 'numpy.ndarray'>",
":serialized:": "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"
},
"_last_episode_starts": {
":type:": "<class 'numpy.ndarray'>",
":serialized:": "gAWV1wAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS2SFlIwBQ5R0lFKULg=="
},
"_last_original_obs": null,
"_episode_num": 0,
"use_sde": false,
"sde_sample_freq": -1,
"_current_progress_remaining": -0.02400000000000002,
"ep_info_buffer": {
":type:": "<class 'collections.deque'>",
":serialized:": "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"
},
"ep_success_buffer": {
":type:": "<class 'collections.deque'>",
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
},
"_n_updates": 400,
"n_steps": 1024,
"gamma": 0.999,
"gae_lambda": 0.98,
"ent_coef": 0.01,
"vf_coef": 0.5,
"max_grad_norm": 0.5,
"batch_size": 128,
"n_epochs": 20,
"clip_range": {
":type:": "<class 'function'>",
":serialized:": "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"
},
"clip_range_vf": null,
"normalize_advantage": true,
"target_kl": null
} |