This textbook explains how and why deep reinforcement learning works. It focuses on four main technical areas: heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach, with Python code examples throughout and pointers to online resources on GitHub. The book is suitable for advanced undergraduate and graduate courses in artificial intelligence, machine learning, games, and evolutionary computing, and for self-study by professionals.weiterlesen