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TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play

Autor: Lin, Fanqi, Huang, Shiyu, Pearce, Tim, Chen, Wenze, Tu, Wei-Wei

Multi-agent football poses an unsolved challenge in AI research. Existing work has focused on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In this paper, we develop a multi-agent system to play the full 11 vs.

Externí odkaz: http://arxiv.org/abs/2302.07515

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SIR Epidemics on Evolving Erd\H{o}s-R\'enyi Graphs

Autor: Chen, Wenze, Hou, Yuewen, Yao, Dong

In the standard SIR model, infected vertices infect their neighbors at rate $\lambda$ independently across each edge. They also recover at rate $\gamma$. In this work we consider the SIR-$\omega$ model where the graph structure itself co-evolves with

Externí odkaz: http://arxiv.org/abs/2208.11923

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Akademický článek

Ergodicity for population dynamics by pure-jump noise with switching

Autor: Zhang, Zhenzhong, Chen, Wenze, Yang, Hanlu, Tong, Jinying, Zhao, Xin

Publikováno v: In Nonlinear Analysis: Hybrid Systems November 2024 54

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TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations

Autor: Huang, Shiyu, Chen, Wenze, Zhang, Longfei, Xu, Shizhen, Li, Ziyang, Zhu, Fengming, Ye, Deheng, Chen, Ting, Zhu, Jun

Deep reinforcement learning (DRL) has achieved super-human performance on complex video games (e.g., StarCraft II and Dota II). However, current DRL systems still suffer from challenges of multi-agent coordination, sparse rewards, stochastic environm

Externí odkaz: http://arxiv.org/abs/2110.04507

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