The paper proposes adding a social component—marginal cost—to autonomous vehicle (AV) routing rewards to account for the impact of AVs on human-driven vehicles (HDVs). Using multi-agent reinforcement learning on real-world networks, the authors show that this social reward reduces HDV travel time increases and improves overall system performance compared to purely selfish AV routing. A scalable approach limits counterfactual simulations to the most influential AVs, maintaining performance while cutting computational cost.
By Anastasia Psarou, Mikolaj Rams, Blazej Torbus, Rafal Kucharski
arXiv:2605.25200v3 Announce Type: replace
Abstract: Travel planning in the real world is overwhelmingly a \textit{group} activity, yet existing LLM travel-planning benchmarks reduce it to a single us...
By Xiang Cheng, Yulan Hu, Lulu Zheng, Xiangwen Zhang, Zheng Pan, Xin Li, Yong Liu
The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.
By Hanlin Sun, Jiayang Li
arXiv:2609.35916v1 Announce Type: cross
Abstract: Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions fac...
By Jie Yang, Jiajun Chen, Jiazheng Zhou, Mianqiu Huang, Yining Zheng, Yuxin Wang, Xipeng Qiu
arXiv:2408.15538v4 Announce Type: replace
Abstract: While modern Autonomous Vehicle (AV) systems can develop reliable driving policies under regular traffic conditions, they frequently struggle with...
By Guanren Qiao, Guorui Quan, Jiawei Yu, Shujun Jia, Guiliang Liu
The paper proposes a multi‑agent reinforcement learning framework that dynamically allocates incentives to drivers, encouraging them to take alternative routes. By doing so, it reduces total travel time by up to 16% and CO₂ emissions by 9% under an unlimited budget, while revealing a trade‑off between the two objectives. The study demonstrates that a carefully weighted multi‑objective function can improve both travel time and emissions beyond baseline levels.
By Germ\'an Pardo-Gonz\'alez, Shaghayegh Vosough, Katerina Papadaki, Claudio Roncoli