most citedRDE: A Hybrid Policy Framework for Multi-Agent Path Finding Problem

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cs.RO2025

MASH: Cooperative-Heterogeneous Multi-Agent Reinforcement Learning for Single Humanoid Robot Locomotion

Qi Liu, Xiaopeng Zhang, Mingshan Tan +3

This paper proposes a novel method to enhance locomotion for a single humanoid robot through cooperative-heterogeneous multi-agent deep reinforcement learning (MARL). While most ex…

cs.RO2023★ 1 cited

RDE: A Hybrid Policy Framework for Multi-Agent Path Finding Problem

Jianqi Gao, Yanjie Li, Xiaoqing Yang +1

Multi-agent path finding (MAPF) is an abstract model for the navigation of multiple robots in warehouse automation, where multiple robots plan collision-free paths from the start t…