7 citations · 9 across the 10 of their papers we have counts for
10 papers
Global Policy-Space Response Oracles for Two-Player Zero-Sum Games
Junyu Zhang, Feihong Yang, Jian Wang +2
The Policy-Space Response Oracles (PSRO) framework scales equilibrium computation to large zero-sum games by iteratively expanding a restricted strategy set using deep reinforcemen…
Efficient Reinforcement Learning for Zero-Shot Coordination in Evolving Games
Bingyu Hui, Lebin Yu, Quanming Yao +3
Zero-shot coordination(ZSC), a key challenge in multi-agent game theory, has become a hot topic in reinforcement learning (RL) research recently, especially in complex evolving gam…
Robust Communicative Multi-Agent Reinforcement Learning with Active Defense
Lebin Yu, Yunbo Qiu, Quanming Yao +3
Communication in multi-agent reinforcement learning (MARL) has been proven to effectively promote cooperation among agents recently. Since communication in real-world scenarios is…
Promoting Cooperation in Multi-Agent Reinforcement Learning via Mutual Help
Yunbo Qiu, Yue Jin, Lebin Yu +2
Multi-agent reinforcement learning (MARL) has achieved great progress in cooperative tasks in recent years. However, in the local reward scheme, where only local rewards for each a…
Improving Zero-Shot Coordination Performance Based on Policy Similarity
Lebin Yu, Yunbo Qiu, Quanming Yao +2
Over these years, multi-agent reinforcement learning has achieved remarkable performance in multi-agent planning and scheduling tasks. It typically follows the self-play setting, w…
Low Entropy Communication in Multi-Agent Reinforcement Learning
Lebin Yu, Yunbo Qiu, Qiexiang Wang +2
Communication in multi-agent reinforcement learning has been drawing attention recently for its significant role in cooperation. However, multi-agent systems may suffer from limita…
Sample-Efficient Multi-Agent Reinforcement Learning with Demonstrations for Flocking Control
Yunbo Qiu, Yuzhu Zhan, Yue Jin +2
Flocking control is a significant problem in multi-agent systems such as multi-agent unmanned aerial vehicles and multi-agent autonomous underwater vehicles, which enhances the coo…
Sub-optimal Policy Aided Multi-Agent Reinforcement Learning for Flocking Control
Yunbo Qiu, Yue Jin, Jian Wang +1
Flocking control is a challenging problem, where multiple agents, such as drones or vehicles, need to reach a target position while maintaining the flock and avoiding collisions wi…
Point Cloud Change Detection With Stereo V-SLAM:Dataset, Metrics and Baseline
Zihan Lin, Jincheng Yu, Lipu Zhou +3
Localization and navigation are basic robotic tasks requiring an accurate and up-to-date map to finish these tasks, with crowdsourced data to detect map changes posing an appealing…
Robust Reinforcement Learning under model misspecification
Lebin Yu, Jian Wang, Xudong Zhang
Reinforcement learning has achieved remarkable performance in a wide range of tasks these days. Nevertheless, some unsolved problems limit its applications in real-world control. O…