68 citations · 107 across the 26 of their papers we have counts for
8 papers · 1 filter
Regret Pruning for Learning Equilibria in Simulation-Based Games
Bhaskar Mishra, Cyrus Cousins, Amy Greenwald
In recent years, empirical game-theoretic analysis (EGTA) has emerged as a powerful tool for analyzing games in which an exact specification of the utilities is unavailable. Instea…
Zero-Sum Stochastic Stackelberg Games
Denizalp Goktas, Jiayi Zhao, Amy Greenwald
Zero-sum stochastic games have found important applications in a variety of fields, from machine learning to economics. Work on this model has primarily focused on the computation…
Exploitability Minimization in Games and Beyond
Denizalp Goktas, Amy Greenwald
Pseudo-games are a natural and well-known generalization of normal-form games, in which the actions taken by each player affect not only the other players' payoffs, as in games, bu…
Gradient Descent Ascent in Min-Max Stackelberg Games
Denizalp Goktas, Amy Greenwald
Min-max optimization problems (i.e., min-max games) have attracted a great deal of attention recently as their applicability to a wide range of machine learning problems has become…
Computational and Data Requirements for Learning Generic Properties of Simulation-Based Games
Cyrus Cousins, Bhaskar Mishra, Enrique Areyan Viqueira +1
Empirical game-theoretic analysis (EGTA) is primarily focused on learning the equilibria of simulation-based games. Recent approaches have tackled this problem by learning a unifor…
Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration
Dustin Morrill, Esra'a Saleh, Michael Bowling +1
Neural replicator dynamics (NeuRD) is an alternative to the foundational softmax policy gradient (SPG) algorithm motivated by online learning and evolutionary game theory. The NeuR…