1 citations · 1 across the 2 of their papers we have counts for
4 papers
Heterogeneity in Multi-Agent Reinforcement Learning
Tianyi Hu, Zhiqiang Pu, Yuan Wang +3
Heterogeneity is a fundamental property in multi-agent reinforcement learning (MARL), which is closely related not only to the functional differences of agents, but also to policy…
CoMoE: Contrastive Representation for Mixture-of-Experts in Parameter-Efficient Fine-tuning
Jinyuan Feng, Chaopeng Wei, Tenghai Qiu +2
In parameter-efficient fine-tuning, mixture-of-experts (MoE), which involves specializing functionalities into different experts and sparsely activating them appropriately, has bee…
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…
OMoE: Diversifying Mixture of Low-Rank Adaptation by Orthogonal Finetuning
Jinyuan Feng, Zhiqiang Pu, Tianyi Hu +3
Building mixture-of-experts (MoE) architecture for Low-rank adaptation (LoRA) is emerging as a potential direction in parameter-efficient fine-tuning (PEFT) for its modular design…