activity
20202026
most citedGame-Theoretic Multiagent Reinforcement Learning

145 citations · 155 across the 18 of their papers we have counts for

collaborators
Showing 2024Show all

8 papers · 1 filter

cs.GT2024

Conflux-PSRO: Effectively Leveraging Collective Advantages in Policy Space Response Oracles

Yucong Huang, Jiesong Lian, Mingzhi Wang +2

Policy Space Response Oracle (PSRO) with policy population construction has been demonstrated as an effective method for approximating Nash Equilibrium (NE) in zero-sum games. Exis…

cs.GT2024

Sample-Efficient Regret-Minimizing Double Oracle in Extensive-Form Games

Xiaohang Tang, Chiyuan Wang, Chengdong Ma +3

Extensive-Form Game (EFG) represents a fundamental model for analyzing sequential interactions among multiple agents and the primary challenge to solve it lies in mitigating sample…

cs.CL2024

Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment

Mingzhi Wang, Chengdong Ma, Qizhi Chen +7

Self-play methods have demonstrated remarkable success in enhancing model capabilities across various domains. In the context of Reinforcement Learning from Human Feedback (RLHF),…

cs.MA2024

Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning

Wenzhe Fan, Zishun Yu, Chengdong Ma +3

In multi-agent reinforcement learning, a commonly considered paradigm is centralized training with decentralized execution. However, in this framework, decentralized execution rest…

cs.AI2024

A Survey on Self-play Methods in Reinforcement Learning

Ruize Zhang, Zelai Xu, Chengdong Ma +8

Self-play, a learning paradigm where agents iteratively refine their policies by interacting with historical or concurrent versions of themselves or other evolving agents, has show…

cs.GT2024★ 2 cited

Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles

Jiesong Lian, Yucong Huang, Chengdong Ma +4

For solving zero-sum games involving non-transitivity, a useful approach is to maintain a policy population to approximate the Nash Equilibrium (NE). Previous studies have shown th…

cs.AI2024★ 1 cited

Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems

Zhaowei Zhang, Fengshuo Bai, Mingzhi Wang +3

The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety. While considerable strides h…

cs.CL2024★ 3 cited

Panacea: Pareto Alignment via Preference Adaptation for LLMs

Yifan Zhong, Chengdong Ma, Xiaoyuan Zhang +5

Current methods for large language model alignment typically use scalar human preference labels. However, this convention tends to oversimplify the multi-dimensional and heterogene…