Universal parameter optimisation in games based on SPSA

Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use generic automatic parameter optimisation algorithms are known only for special problems such as the adjustment of the parameters of an evaluation function. The SPSA algorithm (Simultaneous Perturbation Stochastic Approximation) is a generic stochastic gradient method for optimising an objective function when an analytic expression of the gradient is not available, a frequent case in game programs. Further, SPSA in its canonical form is very easy to implement. As such, it is an attractive choice for parameter optimisation in game programs, both due to its generality and simplicity. The goal of this paper is twofold: (i) to introduce SPSA for the game programming community by putting it into a game-programming perspective, and (ii) to propose and discuss several methods that can be used to enhance the performance...

• Type: Journal
• Year: 2006
• Where: ML
• Authors: Levente Kocsis, Csaba Szepesvári
• Keywords: Game Program, Machine Learning, ML 2006, Parameter Optimisation, Parameters