Strategy evaluation in extensive games with importance sampling

Typically agent evaluation is done through Monte Carlo estimation. However, stochastic agent decisions and stochastic outcomes can make this approach inefficient, requiring many samples for an accurate estimate. We present a new technique that can be used to simultaneously evaluate many strategies while playing a single strategy in the context of an extensive game. This technique is based on importance sampling, but utilizes two new mechanisms for significantly reducing variance in the estimates. We demonstrate its effectiveness in the domain of poker, where stochasticity makes traditional evaluation problematic.

• Type: Conference
• Year: 2008
• Where: ICML
• Authors: Michael H. Bowling, Michael Johanson, Neil Burch, Duane Szafron
• Keywords: ICML 2008, Machine Learning, Monte Carlo Estimation, Stochastic Agent Decisions, Stochastic Outcomes