2025-01-14-lin25b.md

title	booktitle	year	volume	series	month	publisher	pdf	url	openreview	abstract	layout	issn	id	tex_title	firstpage	lastpage	page	order	cycles	bibtex_editor	editor	bibtex_author	author	date	address	container-title	genre	issued	extras
Robust Multi-Agent Reinforcement Learning for Autonomous Vehicle in Noisy Highway Environments	Proceedings of the 16th Asian Conference on Machine Learning	2025	260	Proceedings of Machine Learning Research	0	PMLR	https://raw.githubusercontent.com/mlresearch/v260/main/assets/lin25b/lin25b.pdf	https://proceedings.mlr.press/v260/lin25b.html	M97QPUXvxb	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.	inproceedings	2640-3498	lin25b	Robust Multi-Agent Reinforcement Learning for Autonomous Vehicle in Noisy Highway Environments	1320	1335	1320-1335	1320	false	Nguyen, Vu and Lin, Hsuan-Tien	given family Vu Nguyen given family Hsuan-Tien Lin	Lin, Lilan and Nie, Xiaotong and Hou, Jian	given family Lilan Lin given family Xiaotong Nie given family Jian Hou	2025-01-14		Proceedings of the 16th Asian Conference on Machine Learning	inproceedings	date-parts 2025 1 14	label link Supplementary PDF https://raw.githubusercontent.com/mlresearch/v260/main/assets/assets/lin25b/lin25b-supp.pdf