Reinforcement Learning: A Practical Guide to Algorithms delves into the impactful world of reinforcement learning, a key branch of AI. Spanning over five decades, reinforcement learning has significantly advanced AI, offering solutions for planning, budgeting, and strategic decision-making. This book provides a comprehensive understanding of reinforcement learning, focusing on building smart models and agents that adapt to changing requirements.
We cover fundamental and advanced topics, including value-based methods like UCB, SARSA, and Q-learning, as well as function approximation techniques. Additionally, we explore artificial neural networks, LSTD, gradient methods, emphatic TD methods, average reward methods, and policy gradient methods.
With clear explanations, diagrams, and examples, this book ensures that readers can grasp and apply reinforcement learning algorithms to real-world problems effectively. By the end, you will have a solid foundation in both theoretical and practical aspects of reinforcement learning.
