Reinforcement of Learning Theoretical Foundations and Exploration of Multi-Domain Applications

Reinforcement learning, as an important branch of artificial intelligence, with its unique mechanism of learning optimal strategies through the interaction of intelligence with the environment, makes it show great application potential and significance in many fields. This paper focuses on exploring the applications of reinforcement learning in five fields, including game AI, robotics, autonomous driving, healthcare, and finance. In the study, this paper first introduces the main algorithms of reinforcement learning; then, it dissects the research progress of reinforcement learning in five fields, such as realizing complex strategy decision-making in the field of game AI, accomplishing high-precision task planning in the field of robotics, realizing scenario-based decision-making in the field of autonomous driving, assisting in personalized treatment planning in the field of healthcare and aiding risk assessment in the field of finance. At the same time, this thesis also discusses the current technical limitations faced by each field and looks forward to possible future development directions. This paper aims to provide comprehensive references and insights for researchers in related fields, and to promote the further development and application of reinforcement learning techniques in various fields