A Strategic Analysis of Portfolio Compression

K Mayo and MP Wellman 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Extended Abstract, May 2021 (forthcoming). Abstract Portfolio compression, the netting of cycles in a financial network, is employed…

Evolution Strategies for Approximate Solution of Bayesian Games

Z Li and MP Wellman 35th AAAI Conference on Artificial Intelligence, Feb 2021 (forthcoming). Abstract We address the problem of solving complex Bayesian games, characterized by high-dimensional type and action spaces, many (> 2) players,…

Iterative Empirical Game Solving via Single Policy Best Response

M Smith, T Anthony, and MP Wellman 9th International Conference on Learning Representations (ICLR), Spotlight Presentation, May 2021 (forthcoming). Abstract Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning…

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,…