The rapid advancement of artificial intelligence (AI) has ushered in an era of intelligent autonomous systems capable of making complex decisions in dynamic environments. At the core of this transformation lies reinforcement learning (RL), a powerful paradigm that enables agents to learn optimal behaviors through interaction with their surroundings. This monograph aims to provide a comprehensive exploration of reinforcement learning strategies, delving into foundational concepts, key algorithms, and real-world applications in autonomous AI systems. This work is designed for researchers, engineers, and students who seek to deepen their understanding of RL and its applications in robotics, finance, healthcare, and other domains. By blending theoretical insights with practical implementations, this monograph serves as both an academic resource and a reference guide for professionals in the field of artificial intelligence. We extend our gratitude to the many researchers and practitioners whose contributions have shaped the field of reinforcement learning. Their groundbreaking work has laid the foundation for the concepts discussed in this monograph. We also acknowledge the invaluable support of our colleagues, reviewers, and institutions, whose insights and feedback have greatly enriched this work.