Reinforcement Learning in Practice: Muti-applications of Basic Theory

Reinforcement Learning (RL), as an essential branch of machine learning, has garnered significant attention due to its outstanding performance in complex decision-making tasks and dynamic environments, exemplified by notable achievements such as AlphaGo and DeepSeek in recent years. To provide researchers with a comprehensive perspective of current developments in this area, this paper systematically reviews RL from four distinct perspectives: fundamental theoretical frameworks, practical applications, existing limitations, and future outlook. Initially, core concepts and classical algorithms including Q-learning, SARSA, and Temporal Difference (TD) learning are clearly introduced, establishing a solid theoretical foundation. Then, practical implementations of RL are elaborated within three representative fields: path planning, voltage control and game theory, highlighting their significance and effectiveness through literature analysis. Moreover, this paper critically analyzes the limitations of current RL techniques, such as low sample efficiency, unstable training processes, and insufficient generalization. Finally, potential research directions, including hybrid learning paradigms and multi-agent collaborations, are proposed to inspire future advancements. The insights provided in this review aim to stimulate further theoretical innovation and practical breakthroughs in reinforcement learning.