Reinforcement learning in feedback controlChallenges and benchmarks from technical process control

被引:0
|
作者
Roland Hafner
Martin Riedmiller
机构
[1] Albert-Ludwigs University Freiburg,Machine Learning Lab
来源
Machine Learning | 2011年 / 84卷
关键词
Reinforcement learning; Feedback control; Benchmarks; Nonlinear control;
D O I
暂无
中图分类号
学科分类号
摘要
Technical process control is a highly interesting area of application serving a high practical impact. Since classical controller design is, in general, a demanding job, this area constitutes a highly attractive domain for the application of learning approaches—in particular, reinforcement learning (RL) methods. RL provides concepts for learning controllers that, by cleverly exploiting information from interactions with the process, can acquire high-quality control behaviour from scratch.
引用
收藏
页码:137 / 169
页数:32
相关论文
共 50 条
  • [1] Reinforcement learning in feedback control Challenges and benchmarks from technical process control
    Hafner, Roland
    Riedmiller, Martin
    [J]. MACHINE LEARNING, 2011, 84 (1-2) : 137 - 169
  • [2] Concepts and facilities of a neural reinforcement learning control architecture for technical process control
    Riedmiller, M
    [J]. NEURAL COMPUTING & APPLICATIONS, 1999, 8 (04): : 323 - 338
  • [3] Concepts and Facilities of a Neural Reinforcement Learning Control Architecture for Technical Process Control
    M Riedmiller
    [J]. Neural Computing & Applications, 1999, 8 : 323 - 338
  • [4] Feedback Control For Cassie With Deep Reinforcement Learning
    Xie, Zhaoming
    Berseth, Glen
    Clary, Patrick
    Hurst, Jonathan
    van de Panne, Michiel
    [J]. 2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2018, : 1241 - 1246
  • [5] Tutoring Reinforcement Learning via Feedback Control
    De Lellis, Francesco
    Russo, Giovanni
    di Bernardo, Mario
    [J]. 2021 EUROPEAN CONTROL CONFERENCE (ECC), 2021, : 580 - 585
  • [6] Derivative Feedback Control Using Reinforcement Learning
    Zaheer, Muhammad Hamad
    Yoon, Se Young
    Rizvi, Syed Ali Asad
    [J]. 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, 2023, : 7350 - 7355
  • [7] A Novel Approach to Feedback Control with Deep Reinforcement Learning
    Wang, Yuan
    Velswamy, Kirubakaran
    Huang, Biao
    [J]. IFAC PAPERSONLINE, 2018, 51 (18): : 31 - 36
  • [8] Improving robustness of quantum feedback control with reinforcement learning
    Guatto, Manuel
    Susto, Gian Antonio
    Ticozzi, Francesco
    [J]. PHYSICAL REVIEW A, 2024, 110 (01)
  • [9] Reinforcement Learning and Adaptive Dynamic Programming for Feedback Control
    Lewis, Frank L.
    Vrabie, Draguna
    [J]. IEEE CIRCUITS AND SYSTEMS MAGAZINE, 2009, 9 (03) : 32 - 50
  • [10] Offline reinforcement learning for industrial process control: A case from steel
    Deng, Jifei
    Sierla, Seppo
    Sun, Jie
    Vyatkin, Valeriy
    [J]. INFORMATION SCIENCES, 2023, 632 : 221 - 231