  Connected and automated vehicles (CAVs) can alleviate traffic congestion, air
pollution, and improve safety. In this paper, we provide a decentralized
coordination framework for CAVs at a signal-free intersection to minimize
travel time and improve fuel efficiency. We employ a simple yet powerful
reinforcement learning approach, an off-policy temporal difference learning
called Q-learning, enhanced with a coordination mechanism to address this
problem. Then, we integrate a first-in-first-out queuing policy to improve the
performance of our system. We demonstrate the efficacy of our proposed approach
through simulation and comparison with the classical optimal control method
based on Pontryagin's minimum principle.
