25 citations · 29 across the 7 of their papers we have counts for
7 papers
MrCoM: A Meta-Regularized World-Model Generalizing Across Multi-Scenarios
Xuantang Xiong, Ni Mu, Runpeng Xie +8
Model-based reinforcement learning (MBRL) is a crucial approach to enhance the generalization capabilities and improve the sample efficiency of RL algorithms. However, current MBRL…
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents
Haoyuan Jiang, Xuantang Xiong, Ziyue Li +7
Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when ap…
CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control
Jingqing Ruan, Ziyue Li, Hua Wei +5
Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring inte…
X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner
Haoyuan Jiang, Ziyue Li, Hua Wei +5
The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights.…
Balancing Exploration and Exploitation in Hierarchical Reinforcement Learning via Latent Landmark Graphs
Qingyang Zhang, Yiming Yang, Jingqing Ruan +3
Goal-Conditioned Hierarchical Reinforcement Learning (GCHRL) is a promising paradigm to address the exploration-exploitation dilemma in reinforcement learning. It decomposes the so…
GCS: Graph-based Coordination Strategy for Multi-Agent Reinforcement Learning
Jingqing Ruan, Yali Du, Xuantang Xiong +6
Many real-world scenarios involve a team of agents that have to coordinate their policies to achieve a shared goal. Previous studies mainly focus on decentralized control to maximi…
Learning Multi-agent Action Coordination via Electing First-move Agent
Jingqing Ruan, Linghui Meng, Xuantang Xiong +2
Learning to coordinate actions among agents is essential in complicated multi-agent systems. Prior works are constrained mainly by the assumption that all agents act simultaneously…