PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

RSPSA: enhanced parameter optimisation in games
L Kocsis, Csaba Szepesvari and M. H. M. Winands
In: 11th Advances in Computer Games Conference(2006).


Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather di±cult, successful applications of automatic optimisation al- gorithms in game programs are known only for parameters that belong to certain components (e.g. evaluation-function parameters). The SPSA (Simultaneous Perturbation Stochastic Approximation) algorithm is an attractive choice for optimising any kind of parameters of a game pro- gram, both for its generality and its simplicity. It's disadvantage is that it can be very slow. In this article we propose several methods to speed up SPSA, in particular, the combination with RPROP, using common random numbers, antithetic variables and averaging. We test the result- ing algorithm for tuning various types of parameters in two domains, poker and LOA. From the experimental study, we conclude that using SPSA is a viable approach for optimisation in game programs, especially if no good alternative exists for the types of parameters considered.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6360
Deposited By:Csaba Szepesvari
Deposited On:08 March 2010