AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
- Neil Burch, Department of Computing Science, University of Alberta
- Martin Schmid
- Matej Moravcik
- Dustin Morrill
- Michael Bowling, University of Alberta
Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available. The variance of sampled outcomes may make the simple approach of Monte Carlo sampling inadequate. This is the case for agents playing heads-up no-limit Texas hold'em poker, whereman-machine competitions typically involve multiple days of consistent play by multiple players, but still can (and sometimes did) result in statistically insignificant conclusions. In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that exploits an arbitrary heuristic estimate of state value, as well as the explicit strategy of a subset of the agents. Unlike existing techniques which reduce the variance from chance events, or only consider game ending actions, AIVAT reduces the variance both from choices by nature and by players with a known strategy. The resulting estimator produces results that significantly outperform previous state of the art techniques. It was able to reduce the standard deviation of a Texas hold'em poker man-machine match by 85% and consequently requires 44 times fewer games to draw the same statistical conclusion. AIVAT enabled the first statistically significant AI victory against professional poker players in no-limit hold'em.Furthermore, the technique was powerful enough to produce statistically significant results versus individual players, not just an aggregate pool of the players. We also used AIVAT to analyze a short series of AI vs human poker tournaments,producing statistical significant results with as few as 28 matches.
Citation
N. Burch, M. Schmid, M. Moravcik, D. Morrill, M. Bowling. "AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games". National Conference on Artificial Intelligence (AAAI), (ed: Sheila A. McIlraith, Kilian Q. Weinberger), pp 949-956, February 2018.| Keywords: | game theory, variance reduction, poker |
| Category: | In Conference |
| Web Links: | AAAI |
BibTeX
@incollection{Burch+al:AAAI18,
author = {Neil Burch and Martin Schmid and Matej Moravcik and Dustin Morrill
and Michael Bowling},
title = {AIVAT: A New Variance Reduction Technique for Agent Evaluation in
Imperfect Information Games},
Editor = {Sheila A. McIlraith, Kilian Q. Weinberger},
Pages = {949-956},
booktitle = {National Conference on Artificial Intelligence (AAAI)},
year = 2018,
}Last Updated: February 24, 2020Submitted by Sabina P
