Reinforcement Learning in Complex Environments for Robotics and Gaming

Reinforcement Learning (RL), which has recently attracted much attention in robotics and games, is effective for training intelligent agents who improve their behavior while interacting with environments. The current article examines critical challenges, including sample inefficiency, stability, and ethics, that negatively impact the usage of RL. In the following two sections, we use two applications, specifically AlphaGo and RoboCup Soccer, to explain how RL can be valuable in dynamic situations and why the traditional method of programming cannot solve the problem. Mitigation strategies suggested include the improvement of the RL algorithms along with the enhancement of the framework of RL with other machine learning techniques. Also, guidelines for the ethical use of RL systems are highlighted to address RL system responsibility and safety. The solutions presented in this paper are feasible due to quantitative examinations of the enhanced performance with further enhancements for future RL applications.