Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph

no code implementations • 26 Mar 2024 • Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Xiaolin Ai

This paper proposes a novel hierarchical MARL model called Hierarchical Cooperation Graph Learning (HCGL) for solving general multi-agent problems.

Clustering Graph Learning +1

Measuring Policy Distance for Multi-Agent Reinforcement Learning

1 code implementation • 20 Jan 2024 • Tianyi Hu, Zhiqiang Pu, Xiaolin Ai, Tenghai Qiu, Jianqiang Yi

Furthermore, we extend MAPD to a customizable version, which can quantify differences among agent policies on specified aspects.

Multi-agent Reinforcement Learning reinforcement-learning

Learning Heterogeneous Agent Cooperation via Multiagent League Training

1 code implementation • 13 Nov 2022 • Qingxu Fu, Xiaolin Ai, Jianqiang Yi, Tenghai Qiu, Wanmai Yuan, Zhiqiang Pu

However, they also come with challenges compared with homogeneous systems for multiagent reinforcement learning, such as the non-stationary problem and the policy version iteration issue.

reinforcement-learning Reinforcement Learning (RL)

A Policy Resonance Approach to Solve the Problem of Responsibility Diffusion in Multiagent Reinforcement Learning

no code implementations • 16 Aug 2022 • Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Xiaolin Ai, Wanmai Yuan

SOTA multiagent reinforcement algorithms distinguish themselves in many ways from their single-agent equivalences.