Strategy exploration in empirical games

Empirical analyses of complex games necessarily focus on a restricted set of strategies, and thus the value of empirical game models depends on effective methods for selectively exploring a space of strategies. We formulate an iterative framework for strategy exploration, and experimentally evaluate an array of generic exploration policies on three games: one infinite game with known analytic solution, and two relatively large empirical games generated by simulation. Policies based on iteratively finding a beneficial deviation or best response to the minimum-regret profile among previously explored strategies perform generally well on the profile-regret measure, although we find that some stochas

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Patrick R. Jordan, L. Julian Schvartzman, Michael P. Wellman
• Keywords: ATAL 2010, Empirical Game Models, Empirical Games, Intelligent Agents, Large Empirical Games