Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study

In this work we analyze Multi-Agent Advantage Actor-Critic (MA2C) a recently proposed multi-agent reinforcement learning algorithm that can be applied to adaptive traffic signal control (ATSC) problems. To evaluate its potential we compare MA2C with Independent Advantage Actor-Critic (IA2C) and other Reinforcement Learning or heuristic based algorithms. Specifically, we analyze MA2C theoretically with the framework provided by non-Markov decision processes, which allows a deeper insight of the algorithm, and we critically examine the effectiveness and the robustness of the method by testing it in two traffic areas located in Bologna (Italy) simulated in SUMO, a software modeling tool for ATSC problems. Our results indicate that MA2C, trained with pseudo-random vehicle flows, is a promising technique able to outperform the alternative methods.