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Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics. (arXiv:2312.11834v1 [cs.MA])

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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.

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