




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…