Convergence and No-Regret in Multiagent Learning

Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the envi- ronment is no longer stationary, thus undermining convergence guaran- tees. Second, learning is often susceptible to deception, where the other agents may be able to exploit a learner's particular dynamics. In the worst case, this could result in poorer performance than if the agent was not learning at all. These challenges are identifiable in the two most com- mon evaluation criteria for multiagent learning algorithms: convergence and regret.