1 citations · 1 across the 11 of their papers we have counts for
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Planner-Conditioned Diffusion for Coordinated Multi-Agent Exploration
Marcus Yu Siong Teo, Jeric Lew, Tanishq Duhan +1
Coordinated multi-agent exploration requires not only efficient individual coverage but also non-redundant coverage across agents over extended planning horizons. Conventional appr…
Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations
He Jiang, Jingtian Yan, Yulun Zhang +5
Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones. While m…
PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
Chengyang He, Tanishq Duhan, Gadiel Sznaier Camps +6
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-gu…
COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
Yifeng Zhang, Jieming Chen, Tingguang Zhou +4
Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency i…
CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem
Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan +4
Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting ca…