MultiAgent Reinforcement Learning: Foundations and Modern Approaches

The first comprehensive introduction to MultiAgent Reinforcement Learning (MARL), covering MARLs models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.MultiAgent Reinforcement Learning (MARL), an area of machine learning in which a collective of agents learn to optimally interact in a shared environment, boasts a growing array of applications in modern life, from autonomous driving and multirobot factories to automated trading and energy network management. This text provides a lucid and rigorous introduction to the models, solution concepts, algorithmic ideas, technical challenges, and modern approaches in MARL. The book first introduces the fields foundations, including basics of reinforcement learning theory and algorithms, interactive game models, different solution concepts for games, and the algorithmic ideas underpinning MARL research. It then details contemporary MARL algorithms which leverage deep learning techniques, covering ideas such as centralized training with decentralized execution, value decomposition, parameter sharing, and selfplay. The book comes with its own MARL codebase written in Python, containing implementations of MARL algorithms that are selfcontained and easy to read. Technical content is explained in easytounderstand language and illustrated with extensive examples, illuminating MARL for newcomers while offering highlevel insights for more advanced readers.First textbook to introduce the foundations and applications of MARL, written by experts in the field Integrates reinforcement learning, deep learning, and game theory Practical focus covers considerations for running experiments and describes environments for testing MARL algorithms Explains complex concepts in clear and simple language Classroomtested, accessible approach suitable for graduate students and professionals across computer science, artificial intelligence, and robotics Resources include code and slides