3 citations · 5 across the 7 of their papers we have counts for
7 papers
Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Qingxu Fu, Zhiqiang Pu, Min Chen +2
Large-scale heterogeneous multiagent systems feature various realistic factors in the real world, such as agents with diverse abilities and overall system cost. In comparison to ho…
Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph
Qingxu Fu, Tenghai Qiu, Jianqiang Yi +2
Multi-Agent Reinforcement Learning (MARL) has been successful in solving many cooperative challenges. However, classic non-hierarchical MARL algorithms still cannot address various…
Learning Heterogeneous Agent Cooperation via Multiagent League Training
Qingxu Fu, Xiaolin Ai, Jianqiang Yi +3
Many multiagent systems in the real world include multiple types of agents with different abilities and functionality. Such heterogeneous multiagent systems have significant practi…
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement Learning
Qingxu Fu, Tenghai Qiu, Zhiqiang Pu +2
Multiagent reinforcement learning (MARL) can solve complex cooperative tasks. However, the efficiency of existing MARL methods relies heavily on well-defined reward functions. Mult…
A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning
Qingxu Fu, Tenghai Qiu, Jianqiang Yi +3
SOTA multiagent reinforcement algorithms distinguish themselves in many ways from their single-agent equivalences. However, most of them still totally inherit the single-agent expl…
Concentration Network for Reinforcement Learning of Large-Scale Multi-Agent Systems
Qingxu Fu, Tenghai Qiu, Jianqiang Yi +2
When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to moti…
Learning an Adaptive Model for Extreme Low-light Raw Image Processing
Qingxu Fu, Xiaoguang Di, Yu Zhang
Low-light images suffer from severe noise and low illumination. Current deep learning models that are trained with real-world images have excellent noise reduction, but a ratio par…