, , , , ,

Yongzhao Wang defends dissertation proposal

On 10 May 2022, Yongzhao Wang presented and successfully defended his dissertation proposal titled "Multi-agent Learning by Iterative Refinement of Game Models." The dissertation committee comprises: Michael Wellman [chair] Tilman…

Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis

Y Wang, Q Ma and MP Wellman 21st International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2022 (Forthcoming). Abstract In empirical game-theoretic analysis (EGTA), game models are extended iteratively through a process…

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-21), pages 484–489, December 2021. Abstract In many policy-learning applications, the agent may execute a set of actions…

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,…