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.

翻译：本文提出了一种针对具有非对称滞后的非线性节流阀的学习型控制策略，该策略无需任何先验环境知识即可实现近乎最优的控制。我们从一个经过精心调校的比例积分（PI）控制器出发，利用近期在引导式强化学习（RL）领域的进展，通过从与阀门的额外交互中学习，以改善闭环控制性能。我们在三种不同阀门上的多种场景中测试了所提出的控制方法，所有结果均突显了结合PI与RL框架以提升非线性随机系统控制性能的优势。在所有实验测试案例中，所得智能体比传统RL智能体具有更好的样本效率，并且性能优于PI控制器。