A Model-Based Reinforcement Learning Approach for PID Design

2022-06-07 · Hozefa Jesawada, Amol Yerudkar, Carmen Del Vecchio, Navdeep Singh

Proportional-integral-derivative (PID) controller is widely used across various industrial process control applications because of its straightforward implementation. However, it can be challenging to fine-tune the PID parameters in practice to achieve robust performance. The paper proposes a model-based reinforcement learning (RL) framework to design PID controllers leveraging the probabilistic inference for learning control (PILCO) method and Kullback-Leibler divergence (KLD). Since PID controllers have a much more interpretable control structure than a network basis function, an optimal policy given by PILCO is transformed into a set of robust PID tuning parameters for underactuated mechanical systems. The presented method is general and can blend with several model-based and model-free algorithms. The performance of the devised PID controllers is demonstrated with simulation studies for a benchmark cart-pole system under disturbances and system parameter uncertainties.

📄 PDF Abstract BibTeX arXiv:2206.03567

Code (0)

등록된 구현이 없습니다.

Tasks

Model-based Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective

2022-01-21 · Ryan K. Tan, Yang Liu, Lei Xie

Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potenti…

Drug Design Drug Discovery reinforcement-learning Reinforcement Learning+1

Unsupervised Meta-Learning for Reinforcement Learning

2018-06-12 · ICLR 2020 1 · Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, Sergey Levine

Meta-learning algorithms use past experience to learn to quickly solve new tasks. In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more ef…

Meta-Learning Meta Reinforcement Learning Multi-Task Learning reinforcement-learning+2

Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding

2019-01-04 · Nicholas R. Clayton, Hussein Abbass

The design of reward functions in reinforcement learning is a human skill that comes with experience. Unfortunately, there is not any methodology in the literature that could guide a human to design the reward function o…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep reinforcement learning for process design: Review and perspective

2023-08-15 · Qinghe Gao, Artur M. Schweidtmann

The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial intelligence offer opportunities to accel…

Decision Making Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark

2021-03-29 · Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov 외

The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning. Generalization remains one of the most…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)