2025-01-14-lin25b.md

title	Robust Multi-Agent Reinforcement Learning for Autonomous Vehicle in Noisy Highway Environments
booktitle	Proceedings of the 16th Asian Conference on Machine Learning
year	2025
volume	260
series	Proceedings of Machine Learning Research
month	0
publisher	PMLR
pdf	https://raw.githubusercontent.com/mlresearch/v260/main/assets/lin25b/lin25b.pdf
url	https://proceedings.mlr.press/v260/lin25b.html
openreview	M97QPUXvxb
abstract	The field of research on multi-agent reinforcement learning (MARL) algorithms in self-driving vehicles is rapidly expanding in mixed-traffic scenarios where autonomous vehicles (AVs) and human-driven vehicles (HDVs) coexist. Most studies assume that all AVs can obtain accurate state information. However, in real-world scenarios, noisy sensor measurements have a significant impact. To address this issue, we propose an effective and robust MARL algorithm Multi-Agent Proximal Policy Optimization with Curriculum-based Adversarial Learning (CA-MAPPO) for situations where the observation perturbations are considered. The proposed approach incorporates adversarial samples during training and adopts a curriculum learning approach by gradually increasing the noise intensity. By evaluating the proposed approach in the ideal environment and scenarios under noise attacks with varying intensities, experiment results demonstrate that the proposed algorithm enables AVs to achieve a success rate of over 70% for the multi-lane highway on-ramp merging task, achieving a maximum average speed of up to over 19 $m/s$ and performing significantly better than the state-of-the-art MARL algorithms such as MAPPO and MAACKTR.
layout	inproceedings
issn	2640-3498
id	lin25b
tex_title	Robust Multi-Agent Reinforcement Learning for Autonomous Vehicle in Noisy Highway Environments
firstpage	1320
lastpage	1335
page	1320-1335
order	1320
cycles	false
bibtex_editor	Nguyen, Vu and Lin, Hsuan-Tien
editor	given family Vu Nguyen given family Hsuan-Tien Lin
bibtex_author	Lin, Lilan and Nie, Xiaotong and Hou, Jian
author	given family Lilan Lin given family Xiaotong Nie given family Jian Hou
date	2025-01-14
address
container-title	Proceedings of the 16th Asian Conference on Machine Learning
genre	inproceedings
issued	date-parts 2025 1 14
extras	label link Supplementary PDF https://raw.githubusercontent.com/mlresearch/v260/main/assets/assets/lin25b/lin25b-supp.pdf