Hadi Aghazadeh

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.