  We consider a system to optimize duration of traffic signals using
multi-agent deep reinforcement learning and Vehicle-to-Everything (V2X)
communication. This system aims at analyzing independent and shared rewards for
multi-agents to control duration of traffic lights. A learning agent traffic
light gets information along its lanes within a circular V2X coverage. The
duration cycles of traffic light are modeled as Markov decision Processes. We
investigate four variations of reward functions. The first two are
unshared-rewards: based on waiting number, and waiting time of vehicles between
two cycles of traffic light. The third and fourth functions are: shared-rewards
based on waiting cars, and waiting time for all agents. Each agent has a memory
for optimization through target network and prioritized experience replay. We
evaluate multi-agents through the Simulation of Urban MObility (SUMO)
simulator. The results prove effectiveness of the proposed system to optimize
traffic signals and reduce average waiting cars to 41.5 % as compared to the
traditional periodic traffic control system.
