机器人 / 具身智能

Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations

鲁棒的单智能体强化学习用于应对需求波动的区域交通信号控制

Qiang Li, Jin Niu, Lina Yu

专题命中 模仿学习与强化学习 ：world model(abstract)；分类 cs.AI、cs.LG

AI总结 本文提出一种鲁棒的单智能体强化学习框架，用于应对交通需求波动的区域交通信号控制，通过集中决策和高效学习模型有效减少交通队列长度。

Comments A critical error in the methodology. The reported congestion control effects were not caused by the proposed signal timing optimization, but by an incorrect traffic volume scaling factor during evaluation. The traffic demand was not properly amplified, resulting in misleading performance gains. Due to the substantial nature of the error, completion of revisions is not feasible in the short term

Scalable Multiagent Reinforcement Learning with Collective Influence Estimation

可扩展的多智能体强化学习与集体影响估计

Zhenglong Luo, Zhiyong Chen, Aoxiang Liu, Ke Pan

机构 * School of Engineering, The University of Newcastle（工程学院） ； School of Automation, Central South University（自动化学院） 纠错

专题命中 模仿学习与强化学习 ：robotic(abstract)；分类 cs.LG

AI总结 本文提出了一种可扩展的多智能体强化学习框架，通过集体影响估计网络实现高效协作，避免网络扩展并提升鲁棒性和部署可行性。