Reinforcement Learning in Machine Learning

Abstract

Reinforcement Learning (RL) is a machine-learning approach for problems in which decisions are made repeatedly and their quality becomes clear through feedback. Instead of depending on a fixed collection of labelled answers, an RL agent interacts with an environment, observes its changing state, receives rewards or penalties, and improves its decision policy through experience. This review examines the basic RL framework and discusses deep reinforcement learning, offline learning, multi-agent systems, and human-guided learning. Applications in robotics, games, education, finance, healthcare, and autonomous systems are considered. The review also identifies sample inefficiency, computational cost, instability, delayed rewards, limited generalization, and safety as important challenges. The paper concludes by identifying research opportunities for efficient, reliable, explainable, and adaptable RL systems.