Handbook of Reinforcement Learning and Control

توضیحات

This handbook provides an in-depth exploration of reinforcement learning and control theory, highlighting methods for decision-making, policy optimization, and adaptive control in dynamic environments. It covers classical and modern RL algorithms, model-based and model-free approaches, and their integration with control systems. Through theoretical explanations, practical examples, and case studies, readers learn how to apply RL to robotics, autonomous systems, and industrial processes. The book also discusses challenges such as stability, convergence, and scalability in complex systems. By the end, readers gain a thorough understanding of reinforcement learning principles and practical tools to implement intelligent control solutions.