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采用分类经验回放的深度确定性策略梯度方法

时圣苗 刘全

时圣苗, 刘全. 采用分类经验回放的深度确定性策略梯度方法. 自动化学报, 2022, 48(7): 1816−1823 doi: 10.16383/j.aas.c190406
引用本文: 时圣苗, 刘全. 采用分类经验回放的深度确定性策略梯度方法. 自动化学报, 2022, 48(7): 1816−1823 doi: 10.16383/j.aas.c190406
Shi Sheng-Miao, Liu Quan. Deep deterministic policy gradient with classified experience replay. Acta Automatica Sinica, 2022, 48(7): 1816−1823 doi: 10.16383/j.aas.c190406
Citation: Shi Sheng-Miao, Liu Quan. Deep deterministic policy gradient with classified experience replay. Acta Automatica Sinica, 2022, 48(7): 1816−1823 doi: 10.16383/j.aas.c190406

采用分类经验回放的深度确定性策略梯度方法

doi: 10.16383/j.aas.c190406
基金项目: 国家自然科学基金 (61772355, 61702055, 61876217, 62176175), 江苏高校优势学科建设工程项目资助
详细信息
    作者简介:

    时圣苗:苏州大学计算机科学与技术学院硕士研究生. 主要研究方向为深度强化学习.E-mail: 20175227045@stu.suda.edu.cn

    刘全:苏州大学教授. 主要研究方向为深度强化学习, 自动推理. 本文通信作者.E-mail: quanliu@suda.edu.cn

Deep Deterministic Policy Gradient With Classified Experience Replay

Funds: Supported by National Natural Science Foundation of China (61772355, 61702055, 61876217, 62176175) and Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
More Information
    Author Bio:

    SHI Sheng-Miao Master student at the School of Computer Science and Technology, Soochow University. His main research interest is deep reinforcement learning

    LIU Quan Professor at Soochow University. His research interest covers deep reinforcement learning and automated reasoning. Corresponding author of this paper

  • 摘要: 深度确定性策略梯度(Deep deterministic policy gradient, DDPG)方法在连续控制任务中取得了良好的性能表现. 为进一步提高深度确定性策略梯度方法中经验回放机制的效率, 提出分类经验回放方法, 并采用两种方式对经验样本分类: 基于时序差分误差样本分类的深度确定性策略梯度方法(DDPG with temporal difference-error classification, TDC-DDPG)和基于立即奖赏样本分类的深度确定性策略梯度方法(DDPG with reward classification, RC-DDPG).在TDC-DDPG和RC-DDPG方法中, 分别使用两个经验缓冲池, 对产生的经验样本按照重要性程度分类存储, 网络模型训练时通过选取较多重要性程度高的样本加快模型学习. 在连续控制任务中对分类经验回放方法进行测试, 实验结果表明, 与随机选取经验样本的深度确定性策略梯度方法相比, TDC-DDPG和RC-DDPG方法具有更好的性能.
  • 图  1  CER-DDPG算法结构示意图

    Fig.  1  CER-DDPG algorithm structure diagram

    图  2  实验效果对比图

    Fig.  2  Comparison of experimental results

    图  3  CER-DDPG与最新策略梯度算法的实验对比

    Fig.  3  Experimental comparison of CER-DDPG with the latest policy gradient algorithm

    表  1  连续动作任务中实验数据

    Table  1  Experimental data in continuous action tasks

    任务名称 算法 平均奖赏 最高奖赏 标准差
    HalfCheetah DDPG 3 360.32 5 335.23 1 246.40
    TDC-DDPG 5 349.64 9 220.27 2 368.13
    RC-DDPG 3 979.64 6 553.49 1 580.21
    Ant DDPG 551.87 1 908.30 307.86
    TDC-DDPG 521.42 1 863.99 296.91
    RC-DDPG 772.37 2 971.63 460.05
    Humanoid DDPG 404.36 822.11 114.38
    TDC-DDPG 462.65 858.34 108.20
    RC-DDPG 440.30 835.75 100.31
    Walker DDPG 506.10 1 416.00 243.02
    TDC-DDPG 521.58 1 919.15 252.95
    RC-DDPG 700.57 3 292.62 484.65
    Hopper DDPG 422.10 1 224.68 180.04
    TDC-DDPG 432.64 1 689.48 223.61
    RC-DDPG 513.45 2 050.72 257.82
    Swimmer DDPG 34.06 63.16 16.74
    TDC-DDPG 44.18 69.40 19.77
    RC-DDPG 38.44 71.70 21.59
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-24
  • 录用日期:  2019-09-24
  • 网络出版日期:  2022-06-10
  • 刊出日期:  2022-07-01

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