6 papers
Double Oracle Neural Architecture Search for Game Theoretic Deep Learning Models
Aye Phyu Phyu Aung, Xinrun Wang, Ruiyu Wang +4
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) whe…
In-Context Exploiter for Extensive-Form Games
Shuxin Li, Chang Yang, Youzhi Zhang +5
Nash equilibrium (NE) is a widely adopted solution concept in game theory due to its stability property. However, we observe that the NE strategy might not always yield the best re…
Configurable Mirror Descent: Towards a Unification of Decision Making
Pengdeng Li, Shuxin Li, Chang Yang +5
Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold'em poker, and mixed cooperative and c…
Reinforcement Nash Equilibrium Solver
Xinrun Wang, Chang Yang, Shuxin Li +4
Nash Equilibrium (NE) is the canonical solution concept of game theory, which provides an elegant tool to understand the rationalities. Though mixed strategy NE exists in any game…
Grasper: A Generalist Pursuer for Pursuit-Evasion Problems
Pengdeng Li, Shuxin Li, Xinrun Wang +5
Pursuit-evasion games (PEGs) model interactions between a team of pursuers and an evader in graph-based environments such as urban street networks. Recent advancements have demonst…
Self-adaptive PSRO: Towards an Automatic Population-based Game Solver
Pengdeng Li, Shuxin Li, Chang Yang +4
Policy-Space Response Oracles (PSRO) as a general algorithmic framework has achieved state-of-the-art performance in learning equilibrium policies of two-player zero-sum games. How…