Building Action Sets in a Deep Reinforcement Learner

Y Wang, A Sinha, S CH-Wang, and MP Wellman 20th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2021 (forthcoming). Abstract In many policy-learning applications, the agent may execute a set of actions…

A Strategic Analysis of Portfolio Compression

K Mayo and MP Wellman 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Extended Abstract, pages 1599-1601, May 2021. 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, pages 5531-5540, Feb 2021. 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. Abstract Policy-Space Response Oracles (PSRO) is a general algorithmic framework for learning policies in…

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…