Decentralized Multi Agent Deep Reinforcement Q-Learning for Intelligent Traffic Controller

Recent development of deep reinforcement learning models has impacted many fields, especially decision based control systems. Urban traffic signal control minimizes traffic congestion as well as overall traffic delay. In this work, we use a decentralized multi-agent reinforcement learning model represented by a novel state and reward function. In comparison to other single agent models reported in literature, this approach uses minimal data collection to control the traffic lights. Our model is assessed using traffic data that has been synthetically generated. Additionally, we compare the outcomes to those of existing models and employ the Monaco SUMO Traffic (MoST) Scenario to examine real-time traffic data. Finally, we use statistical model checking (specifically, the Multi-VeStA) to check performance properties. Our model works well in all synthetic generated data and real time data.