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