Title : Multi-Agent Variational Bayes 

Jun Wang

Abstract :
Multi-agent learning arises in a variety of domains where intelligent agents interact not only with the (unknown) environment but also, critically, with each other. It has an increasing number of applications ranging from controlling a group of autonomous vehicles to coordinating collaborative bots in production lines, optimising distributed sensor networks, and machine bidding in competitive e-commerce and financial markets, just to name a few. Yet, the non-stationary nature calls for a new theory that brings interactions into the learning process. In this talk, I shall provide an up-to-date introduction on the theory and methods of multi-agent AI, with a focus on multiagent learning and reasoning framework on the basis of Bayesian decision making. The studies in both game theory and machine learning will be examined in a unified treatment, making use of variational inference. I shall also sample our recent work on the subject including mean-field multiagent reinforcement learning, theory of mind and recursive reasoning, and solution concepts beyond Nash-equilibrium.