  Connected Autonomous Vehicles will make autonomous intersection management a
reality replacing traditional traffic signal control. Autonomous intersection
management requires time and speed adjustment of vehicles arriving at an
intersection for collision-free passing through the intersection. Due to its
computational complexity, this problem has been studied only when vehicle
arrival times towards the vicinity of the intersection are known beforehand,
which limits the applicability of these solutions for real-time deployment. To
solve the real-time autonomous traffic intersection management problem, we
propose a reinforcement learning (RL) based multiagent architecture and a novel
RL algorithm coined multi-discount Q-learning. In multi-discount Q-learning, we
introduce a simple yet effective way to solve a Markov Decision Process by
preserving both short-term and long-term goals, which is crucial for
collision-free speed control. Our empirical results show that our RL-based
multiagent solution can achieve near-optimal performance efficiently when
minimizing the travel time through an intersection.
