### Structure learning for approximate solution of many-player games

Z Li and MP Wellman 34th AAAI Conference on Artificial Intelligence, pages 2119-2127, Feb 2020. Abstract Games with many players are difficult to solve or even specify without adopting structural assumptions that enable representation in…

### Iterated Deep Reinforcement Learning in Games: History-Aware Training for Improved Stability

M Wright, Y Wang, and MP Wellman Proceedings of the 20th ACM Conference on Economics and Computation, pages 617-636, June 2019. Abstract Deep reinforcement learning (RL) is a powerful method for generating policies in complex environments,…

### Deception in Finitely Repeated Security Games

TH Nguyen, Y Wang, A Sinha, and MP Wellman 33rd AAAI Conference on Artificial Intelligence, Jan/Feb 2019. Abstract Allocating resources to defend targets from attack is often complicated by uncertainty about the attacker’s capabilities,…

### Bounding regret in empirical games

S Jecmen, A Sinha, Z Li, L Tran-Thanh 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. Abstract Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack…