1 citations · 3 across the 15 of their papers we have counts for
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Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning
Tianyi Hu, Qingxu Fu, Zhiqiang Pu +2
In this paper, we propose Unreal Multi-Agent Playground (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent…
Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning
Hao Ma, Shijie Wang, Zhiqiang Pu +2
Guiding the policy of multi-agent reinforcement learning to align with human common sense is a difficult problem, largely due to the complexity of modeling common sense as a reward…
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
Hao Ma, Tianyi Hu, Zhiqiang Pu +4
Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominan…
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…
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…