Computational Intelligent Techniques in Mechatronics

5Reinforcement Learning for Adaptive Mechatronics Systems

D. Sathya¹*, G. Saravanan² and R. Thangamani¹

¹Department of Artificial Intelligence, Kongu Engineering College, Tamil Nadu, India

²Department of Computer Science and Engineering, Erode Sengunthar Engineering College, Tamil Nadu, India

Abstract

Reinforcement Learning (RL) has emerged as a promising and powerful approach for developing adaptive mechatronics systems, capable of learning from interactions with their environment and dynamically adjusting their behavior to achieve desired objectives. This abstract explores the application of RL in the context of mechatronics, focusing on its ability to optimize control strategies, enhance system performance, and enable autonomous adaptation in response to changing conditions. The abstract begins with an introduction to mechatronics, emphasizing the need for adaptive systems that can continuously improve their performance and respond to uncertainties in real-world scenarios. It highlights the limitations of traditional control methods in handling complex and dynamic environments, underscoring the potential of RL to overcome these challenges. The core principles of RL are discussed, shedding light on its fundamental components, including the agent, environment, and reward system. RL algorithms [1], such as Q-learning, Deep Q-Networks (DQNs), and Proximal Policy Optimization (PPO), are explored, showcasing their ability to enable learning and decision-making in mechatronic systems. ...