Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics

In recent years, simulations of pedestrians using multi-agent reinforcement learning (MARL) have been studied. This study considers roads in a grid-world environment and implements pedestrians as MARL agents using an echo-state network and the least squares policy iteration method. In this environment, the ability of these agents to learn to move forward by avoiding other agents is investigated. Specifically, we consider two types of tasks: the choice between a narrow direct route and a broad detour and the bidirectional pedestrian flow in a corridor. The simulation results indicate that the learning is successful when the density of agents is not that high.