Applied Reinforcement Learning

Applied Reinforcement Learning presents RL in an intuitive way, effectively applying this powerful technique in real-world environments. Each chapter explores an end-to-end industry case study—including optimizing an ad campaign using contextual bandit algorithms, production line scheduling problems using tabular RL and Deep Q-Networks for real-world business challenges, and applying dynamic pricing with Deep Deterministic Policy Gradient for solving dynamic pricing problems. For each example, you’ll step into the role of a consultant, analyzing how a problem can be effectively solved with RL. You’ll discover full coverage of the latest and most-relevant techniques for RL, including utilizing Reinforcement Learning with Human Feedback (RLHF) to align Large Language Models into business objectives and constraints.