Overview

Introducing a new approach to multiagent reinforcement learning and distributed artificial intelligence, this guide shows how classical game theory can be used to compose basic learning units. This approach to creating agents has the advantage of leading to powerful, yet intuitively simple, algorithms that can be analyzed. The setup is demonstrated here in a number of different settings, with a detailed analysis of agent learning behaviors provided for each. A review of required background materials from game theory and reinforcement learning is also provided, along with an overview of related multiagent learning methods.

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Author Information

Peter Vrancx is a postdoctoral researcher at the Computational Modeling Lab (CoMo) at the ASP-VUB. His research interests include multiagent learning, reinforcement learning, game theory, and swarm intelligence.

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