2016/12/20 by Neil Burch, Martin Schmid, Burch, Neil +5 · 4 citations
Computer Science · Economics, Econometrics and Finance · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #FOS: Computer and information sciences #Sports Analytics and Performance #cs.AI
paper · pdf · doi:10.48550/arxiv.1612.06915
To appear at AAAI-17 Workshop on Computer Poker and Imperfect Information Games
openalex publication_date 2016/12/20 · arxiv created 2017/01/19 · arxiv updated 2017/01/23 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
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, where man-machine competitions have involved multiple days of consistent play and still not resulted in statistically significant conclusions even when the winner's margin is substantial. In this paper, we introduce AIVAT, a low variance, provably unbiased value assessment tool that uses 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 in no-limit poker can reduce the number of hands needed to draw statistical conclusions by more than a factor of 10.