21 citations · 22 across the 4 of their papers we have counts for
5 papers
Computing Perfect Bayesian Equilibria, with Application to Empirical Game-Theoretic Analysis
Christine Konicki, Mithun Chakraborty, Michael P. Wellman
Perfect Bayesian Equilibrium (PBE) is a refinement of the Nash equilibrium for imperfect-information extensive-form games (EFGs) that enforces consistency between the two component…
Policy Abstraction and Nash Refinement in Tree-Exploiting PSRO
Christine Konicki, Mithun Chakraborty, Michael P. Wellman
Policy Space Response Oracles (PSRO) interleaves empirical game-theoretic analysis with deep reinforcement learning (DRL) to solve games too complex for traditional analytic method…
Learning Bayesian Game Families, with Application to Mechanism Design
Madelyn Gatchel, Michael P. Wellman
Learning or estimating game models from data typically entails inducing separate models for each setting, even if the games are parametrically related. In empirical mechanism desig…
A Meta-Game Evaluation Framework for Deep Multiagent Reinforcement Learning
Zun Li, Michael P. Wellman
Evaluating deep multiagent reinforcement learning (MARL) algorithms is complicated by stochasticity in training and sensitivity of agent performance to the behavior of other agents…
Empirical Game-Theoretic Analysis: A Survey
Michael P. Wellman, Karl Tuyls, Amy Greenwald
In the empirical approach to game-theoretic analysis (EGTA), the model of the game comes not from declarative representation, but is derived by interrogation of a procedural descri…