19 citations · 47 across the 13 of their papers we have counts for
13 papers
Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning
Jiayu Chen, Chao Yu, Guosheng Li +6
Multi-UAV pursuit-evasion, where pursuers aim to capture evaders, poses a key challenge for UAV swarm intelligence. Multi-agent reinforcement learning (MARL) has demonstrated poten…
CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control
Jinwei Zeng, Chao Yu, Xinyi Yang +6
City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particula…
LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination
Jijia Liu, Chao Yu, Jiaxuan Gao +4
AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI co…
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments
Jiayu Chen, Guosheng Li, Chao Yu +4
This paper addresses multi-UAV pursuit-evasion, where a group of drones cooperates to capture a fast evader in a confined environment with obstacles. Existing heuristic algorithms,…
MASP: Scalable GNN-based Planning for Multi-Agent Navigation
Xinyi Yang, Xinting Yang, Chao Yu +4
We investigate multi-agent navigation tasks, where multiple agents need to reach initially unassigned goals in a limited time. Classical planning-based methods suffer from expensiv…
Active Neural Topological Mapping for Multi-Agent Exploration
Xinyi Yang, Yuxiang Yang, Chao Yu +5
This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. A po…
Fictitious Cross-Play: Learning Global Nash Equilibrium in Mixed Cooperative-Competitive Games
Zelai Xu, Yancheng Liang, Chao Yu +2
Self-play (SP) is a popular multi-agent reinforcement learning (MARL) framework for solving competitive games, where each agent optimizes policy by treating others as part of the e…
Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Game
Zelai Xu, Chao Yu, Fei Fang +2
Agents built with large language models (LLMs) have shown great potential across a wide range of domains. However, in complex decision-making tasks, pure LLM-based agents tend to e…
Accelerate Multi-Agent Reinforcement Learning in Zero-Sum Games with Subgame Curriculum Learning
Jiayu Chen, Zelai Xu, Yunfei Li +6
Learning Nash equilibrium (NE) in complex zero-sum games with multi-agent reinforcement learning (MARL) can be extremely computationally expensive. Curriculum learning is an effect…
OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control
Botian Xu, Feng Gao, Chao Yu +3
In this work, we introduce OmniDrones, an efficient and flexible platform tailored for reinforcement learning in drone control, built on Nvidia's Omniverse Isaac Sim. It employs a…
LAGOON: Language-Guided Motion Control
Shusheng Xu, Huaijie Wang, Jiaxuan Gao +3
We aim to control a robot to physically behave in the real world following any high-level language command like "cartwheel" or "kick". Although human motion datasets exist, this ta…
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
Chao Yu, Jiaxuan Gao, Weilin Liu +5
There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an agent that can cooperate with humans in a zero-shot fashion without using any human data.…
Multi-Agent Vulnerability Discovery for Autonomous Driving with Hazard Arbitration Reward
Weilin Liu, Ye Mu, Chao Yu +6
Discovering hazardous scenarios is crucial in testing and further improving driving policies. However, conducting efficient driving policy testing faces two key challenges. On the…