mlsanityData to 7 Oct 2026

Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark

arXiv 2402.13654 ↗First posted 21 Feb 2024eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment. We start with a carefully tuned Proportional Integrator (PI) controller and exploit the recent advances in Reinforcement Learning (RL) with Guides to improve the closed-loop behavior by learning from the additional interactions with the valve. We test the proposed control method in various scenarios on three different valves, all highlighting the benefits of combining both PI and RL frameworks to improve control performance in non-linear stochastic systems. In all the experimental test cases, the resulting agent has a better sample efficiency than traditional RL agents and outperforms the PI controller.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 21 Feb 2024.

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14 of 32 references.

References · 14

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  3. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  4. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  5. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  6. 2020dm_control: Software and Tasks for Continuous Controlcited by 368
  7. 2018Residual Reinforcement Learning for Robot Controlcited by 242
  8. 2018A Tour of Reinforcement Learning: The View from Continuous Controlcited by 129
  9. 2022Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progresscited by 63
  10. 2018Reinforcement Learning for UAV Attitude Controlcited by 40
  11. 2020Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problemcited by 4
  12. 2020Combining Deep Reinforcement Learning And Local Control For The Acrobot Swing-up And Balance Taskcited by 4
2 more
  1. 2020Reinforcement Learning for Control of Valvescited by 2
  2. 2023Aiding reinforcement learning for set point controlcited by 1

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