42 citations · 113 across the 13 of their papers we have counts for
14 papers
No-Regret Strategy Solving in Imperfect-Information Games via Pre-Trained Embedding
Yanchang Fu, Shengda Liu, Pei Xu +1
High-quality information set abstraction remains a core challenge in solving large-scale imperfect-information extensive-form games (IIEFGs)--such as no-limit Texas Hold'em--where…
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…
SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling
Xingzhou Lou, Junge Zhang, Jian Xie +3
Human preference alignment is critical in building powerful and reliable large language models (LLMs). However, current methods either ignore the multi-dimensionality of human pref…
Signal Observation Models and Historical Information Integration in Poker Hand Abstraction
Yanchang Fu, Pei Xu, Dongdong Bai +2
Hand abstraction has been instrumental in developing powerful AI for Texas Hold'em poker, a widely studied testbed for imperfect information games (IIGs). Despite its success, the…
TAPE: Leveraging Agent Topology for Cooperative Multi-Agent Policy Gradient
Xingzhou Lou, Junge Zhang, Timothy J. Norman +2
Multi-Agent Policy Gradient (MAPG) has made significant progress in recent years. However, centralized critics in state-of-the-art MAPG methods still face the centralized-decentral…
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models
Xingzhou Lou, Junge Zhang, Ziyan Wang +2
Safe reinforcement learning (RL) agents accomplish given tasks while adhering to specific constraints. Employing constraints expressed via easily-understandable human language offe…
See Your Heart: Psychological states Interpretation through Visual Creations
Likun Yang, Xiaokun Feng, Xiaotang Chen +2
In psychoanalysis, generating interpretations to one's psychological state through visual creations is facing significant demands. The two main tasks of existing studies in the fie…
PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination
Xingzhou Lou, Jiaxian Guo, Junge Zhang +3
Zero-shot human-AI coordination holds the promise of collaborating with humans without human data. Prevailing methods try to train the ego agent with a population of partners via s…
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox
Qiyue Yin, Tongtong Yu, Shengqi Shen +5
With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficie…
AI in Human-computer Gaming: Techniques, Challenges and Opportunities
Qiyue Yin, Jun Yang, Kaiqi Huang +6
With breakthrough of the AlphaGo, human-computer gaming AI has ushered in a big explosion, attracting more and more researchers all around the world. As a recognized standard for t…
Discriminative Learning of Latent Features for Zero-Shot Recognition
Yan Li, Junge Zhang, Jianguo Zhang +1
Zero-shot learning (ZSL) aims to recognize unseen image categories by learning an embedding space between image and semantic representations. For years, among existing works, it ha…