Deep Reinforcement Learning for Motion Control Algorithms in Robotics

Abstract

This article introduces the concept and practical applications of deep reinforcement learning (DRL), describes the core principles and main algorithms of DRL, and summarizes the advantages of DRL and its differences from traditional reinforcement learning (RL). At the same time, it introduces the broad application prospects of DRL in various fields and lists some examples about the mechanical field. This includes applications for classifying colored objects, agricultural applications for robotic arms to cut fruits, branches, or twigs, a DQN based learning system, a mobile robot that can perform selection and placement operations, A path-planning approach concerning crop picking robotic arms, and a decision algorithm for robot tracking applications, Examples of algorithms for unmanned underwater vehicles and improvements for DRL to solve trajectory tracking control problems in autonomous underwater robots. Finally, the powerful role of DRL in future industrial development and other fields was summarized. Despite the significant progress made in DRL, there are still challenges to be addressed, such as the need for large amounts of training data, the difficulty in designing appropriate reward functions, and the potential for instability during training, which present opportunities for future research and development in this rapidly evolving field.