5 papers
KrwEmd: Revising the Imperfect-Recall Abstraction from Forgetting Everything
Yanchang Fu, Qiyue Yin, Shengda Liu +2
Excessive abstraction is a critical challenge in hand abstraction-a task specific to games like Texas hold'em-when solving large-scale imperfect-information games, as it impairs AI…
Beyond Outcome-Based Imperfect-Recall: Higher-Resolution Abstractions for Imperfect-Information Games
Yanchang Fu, Qiyue Yin, Shengda Liu +2
Hand abstraction is crucial for scaling imperfect-information games (IIGs) such as Texas Hold'em, yet progress is limited by the lack of a formal task model and by evaluations that…
Constructive Conflict-Driven Multi-Agent Reinforcement Learning for Strategic Diversity
Yuxiang Mai, Qiyue Yin, Wancheng Ni +2
In recent years, diversity has emerged as a useful mechanism to enhance the efficiency of multi-agent reinforcement learning (MARL). However, existing methods predominantly focus o…
WGSR-Bench: Wargame-based Game-theoretic Strategic Reasoning Benchmark for Large Language Models
Qiyue Yin, Pei Xu, Qiaozhe Li +15
Recent breakthroughs in Large Language Models (LLMs) have led to a qualitative leap in artificial intelligence' s performance on reasoning tasks, particularly demonstrating remarka…
Improve the efficiency of deep reinforcement learning through semantic exploration guided by natural language
Zhourui Guo, Meng Yao, Yang Yu +1
Reinforcement learning is a powerful technique for learning from trial and error, but it often requires a large number of interactions to achieve good performance. In some domains,…