Learning in Ad-hoc Anti-coordination Scenarios

We present a brief overview of learning dynamics for anti-coordination in ad-hoc scenarios. Specifically, we consider multi-armed bandit algorithms, reinforcement learning, and symmetric strategies for the repeated resource allocation game. In a multi-agent system with dynamic population where every agent is able to learn, the anti-coordination problem exhibits unique challenges. Thus, it is essential for the success of a joint plan that the agents can quickly and robustly learn their optimal behavior. In this work we will focus on convergence rate, efficiency, and fairness in the final outcome.