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

近年来，研究了使用多智能体强化学习（MARL）进行行人模拟。该研究考虑了在一个网格世界环境中的道路，并使用回声状态网络和最小二乘策略迭代方法将行人实现为 MARL 智能体。在这个环境下，探究了这些智能体通过避开其他智能体学会向前移动的能力。具体来说，研究了两种类型的任务：在狭窄的直行路线和宽阔的绕行路线之间进行选择，以及走廊中的双向行人流动。模拟结果表明，当智能体的密度不是很高时，学习是成功的。