Reinforcement Learning in Real-world Applications: Challenges, Successes, and Future Directions

Abstract

Reinforcement Learning (RL) has emerged as a powerful paradigm within machine learning, enabling agents to learn optimal behaviors through interaction with their environment. While initially popularized in game-playing scenarios, RL has rapidly expanded into diverse real-world applications ranging from robotics and finance to healthcare and autonomous driving. This paper reviews the current landscape of RL in real-world applications, highlighting key challenges, successful implementations, and future research directions.