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

arXiv: https://arxiv.org/abs/2312.11834 · HTML: https://mlsanity.com/paper/2312.11834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [A Review of Cooperative Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1908.03963/) (2019)
- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Reinforcement Learning with Convolutional Reservoir Computing](https://mlsanity.com/paper/1912.04161/) (2019)
- [Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach](https://mlsanity.com/paper/1810.11758/) (2018)
- [Recursive Least Squares Policy Control with Echo State Network](https://mlsanity.com/paper/2201.04781/) (2022)

## Built on by (most cited first, 1 of 1)

- [Application of linear regression and quasi-Newton methods to the deep reinforcement learning in continuous action cases](https://mlsanity.com/paper/2503.14976/) (2025)

## Abstract (arXiv)

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.