
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,…