Reinforcement Learning for Decision-Making Agents

A comprehensive introduction to reinforcement learning for building decision-making AI agents. Chapters cover the fundamentals of RL, from Markov decision processes to policy gradients, with emphasis on real-world applications in trading, resource allocation, and strategic planning. You'll learn how to structure problems for RL, design reward functions, and train agents that make optimal decisions in complex environments. Reinforcement learning is the engine behind the most capable AI decision-making systems. This book gives you the theoretical foundation and practical knowledge to build RL agents that make better decisions.