Qiushi Lin

2 accepted papers

2024

MFC-EQ: Mean-Field Control with Envelope Q-learning for Moving Decentralized Agents in Formation

IROS 2024

We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickly while maintaining a desired formation. The agents must balance these objective

Cited by 1 Source ↗Scholar ↗

2023

SACHA: Soft Actor-Critic With Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding

Qiushi Lin, Hang Ma

RA-L 2023

Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning

Cited by 32 Source ↗code ↗Scholar ↗