结合状态预测的深度强化学习交通信号控制

Urban traffic signal control can widely use deep reinforcement learning (DRL) technique. However, in existing researches, most DRL agents only use the current traffic state to make decisions and have limited control effects when the traffic flow changes greatly. Aiming at the problem, this paper proposed a state prediction based deep reinforcement learning algorithm for traffic signal control. The algorithm used one-hot coding to design a concise and efficient traffic state, and then used a Long Short-Term Memory (LSTM) to predict the future state. The agent made optimal decisions based on the current state and the predicted state. The experimental results on the simulation platform SUMO show that compared with three typical signal control algorithms, the proposed algorithm has the best performance in terms of average waiting time, travel time, fuel consumption, CO2 emissions and cumulative reward both in a single intersection and multiple intersections under different flow conditions.