no code implementations • 26 Mar 2024 • Qingxu Fu, Zhiqiang Pu, Min Chen, Tenghai Qiu, Jianqiang Yi
Furthermore, we design a prioritized policy gradient approach to compensate for the gap caused by differences in the number of different types of agents.
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.
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.
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.
1 code implementation • 5 Aug 2022 • Qingxu Fu, Tenghai Qiu, Zhiqiang Pu, Jianqiang Yi, Wanmai Yuan
Next, based on this novel graph structure, we propose a Cooperation Graph Multiagent Reinforcement Learning (CG-MARL) algorithm, which can efficiently deal with the sparse reward problem in multiagent tasks.
no code implementations • 12 Mar 2022 • Qingxu Fu, Tenghai Qiu, Jianqiang Yi, Zhiqiang Pu, Shiguang Wu
Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities.
1 code implementation • 22 Apr 2020 • Qingxu Fu, Xiaoguang Di, Yu Zhang
Furthermore, those tests illustrate that the proposed method is able to adaptively control the global image brightness according to the content of the image scene.