activity
20192026
most citedSupervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings

55 citations · 240 across the 40 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.AI2024

Playable Game Generation

Mingyu Yang, Junyou Li, Zhongbin Fang +5

In recent years, Artificial Intelligence Generated Content (AIGC) has advanced from text-to-image generation to text-to-video and multimodal video synthesis. However, generating pl…

cs.LG2024

Learning Versatile Skills with Curriculum Masking

Yao Tang, Zhihui Xie, Zichuan Lin +2

Masked prediction has emerged as a promising pretraining paradigm in offline reinforcement learning (RL) due to its versatile masking schemes, enabling flexible inference across va…

cs.AI2024★ 1 cited

Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks

Yun Qu, Boyuan Wang, Jianzhun Shao +15

The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre-collected o…

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.CL2024

Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models

Fuxiang Zhang, Junyou Li, Yi-Chen Li +3

Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge…

cs.AI2024★ 1 cited

Reaching Consensus in Cooperative Multi-Agent Reinforcement Learning with Goal Imagination

Liangzhou Wang, Kaiwen Zhu, Fengming Zhu +6

Reaching consensus is key to multi-agent coordination. To accomplish a cooperative task, agents need to coherently select optimal joint actions to maximize the team reward. However…

cs.CL2024★ 21 cited

More Agents Is All You Need

Junyou Li, Qin Zhang, Yangbin Yu +2

We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method, termed as A…

cs.AI2024

Affordable Generative Agents

Yangbin Yu, Qin Zhang, Junyou Li +2

The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged…