most citedMxplainer: Explain and Learn Insights by Imitating Mahjong Agents

1 citations · 1 across the 8 of their papers we have counts for

collaborators
Showing cs.AIShow all

6 papers · 1 filter

cs.AI2026

BotzoneBench: Scalable LLM Evaluation via Graded AI Anchors

Lingfeng Li, Yunlong Lu, Yuefei Zhang +7

Large Language Models (LLMs) are increasingly deployed in interactive environments requiring strategic decision-making, yet systematic evaluation of these capabilities remains chal…

cs.AI2026

Decoupling Return-to-Go for Efficient Decision Transformer

Yongyi Wang, Hanyu Liu, Lingfeng Li +5

The Decision Transformer (DT) has established a powerful sequence modeling approach to offline reinforcement learning. It conditions its action predictions on Return-to-Go (RTG), u…

cs.AI2026

Adapting Rules of Official International Mahjong for Online Players

Chucai Wang, Lingfeng Li, Yunlong Lu +1

As one of the worldwide spread traditional game, Official International Mahjong can be played and promoted online through remote devices instead of requiring face-to-face interacti…

cs.AI2025

Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling

Yongyi Wang, Lingfeng Li, Bozhou Chen +5

Recent benchmarks for memory-augmented reinforcement learning (RL) have introduced partially observable Markov decision process (POMDP) environments in which agents must use histor…

cs.AI2025

Policy Improvement with Style-Specific Demonstrations

Lingfeng Li, Yunlong Lu, Yongyi Wang +1

Proficient game agents with diverse play styles enrich the gaming experience and enhance the replay value of games. However, recent advancements in game AI based on reinforcement l…

cs.AI2025★ 1 cited

Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents

Lingfeng Li, Yunlong Lu, Yongyi Wang +2

People need to internalize the skills of AI agents to improve their own capabilities. Our paper focuses on Mahjong, a multiplayer game involving imperfect information and requiring…