activity
20242026
most citedMxplainer: Explain and Learn Insights by Imitating Mahjong Agents

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

collaborators

9 papers

cs.LG2026

Beyond Autoregressive RTG: Conditioning via Injection Outside Sequential Modeling in Decision Transformer

Yongyi Wang, Hanyu Liu, Lingfeng Li +6

Decision Transformer (DT) formulates offline reinforcement learning as autoregressive sequence modeling, achieving promising results by predicting actions from a sequence of Return…

cs.AI2026

ShuttleEnv: An Interactive Data-Driven RL Environment for Badminton Strategy Modeling

Ang Li, Xinyang Gong, Bozhou Chen +5

We present ShuttleEnv, an interactive and data-driven simulation environment for badminton, designed to support reinforcement learning and strategic behavior analysis in fast-paced…

cs.LG2026

Pareto-guided Pipeline for Distilling Featherweight AI Agents in Mobile MOBA Games

Xionghui Yang, Bozhou Chen, Yunlong Lu +8

Recent advances in game AI have demonstrated the feasibility of training agents that surpass top-tier human professionals in complex environments such as Honor of Kings (HoK), a le…

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

HLSMAC: A New StarCraft Multi-Agent Challenge for High-Level Strategic Decision-Making

Xingxing Hong, Yungong Wang, Dexin Jin +4

Benchmarks are crucial for assessing multi-agent reinforcement learning (MARL) algorithms. While StarCraft II-related environments have driven significant advances in MARL, existin…

cs.AI2025

Constructing Non-Markovian Decision Process via History Aggregator

Yongyi Wang, Wenxin Li

In the domain of algorithmic decision-making, non-Markovian dynamics manifest as a significant impediment, especially for paradigms such as Reinforcement Learning (RL), thereby exe…

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…

cs.NI2024

On Resolving Non-Preemptivity in Multitask Scheduling: An Optimal Algorithm in Deterministic and Stochastic Worlds

Wenxin Li

The efficient scheduling of multi-task jobs across multiprocessor systems has become increasingly critical with the rapid expansion of computational systems. This challenge, known…