Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolutionary Algorithms, have recently managed to beat the state of the art in performance across many games. However, the best results per game are highly dependent on the specific configuration of modifications and hybrids introduced over several works, each described as parameters in the algorithm. However, the search for the best parameters has been reduced to several human-picked combinations, as the possibility space has grown beyond exhaustive search. This paper presents the state of the art in Rolling Horizon Evolutionary algorithms, combining all modifications described in literature and some additional ones for a large resultant hybrid. It then uses a parameter optimiser, the N-Tuple Bandit Evolutionary Algorithm, to find the best combination of parameters in 20 games with various properties from the General Video Game AI Framework. We highlight the noisy optimisation problem resultant, as both the games and the algorithm being optimised are stochastic. We then analyse the algorithm's parameters and interesting combinations revealed through the parameter optimisation process. Lastly, we show that it is possible to automatically explore a large parameter space and find configurations which outperform the state of the art on several games.

Automatic game design is an increasingly popular area of research that consists of devising systems that create content or complete games autonomously. The interest in such systems is two-fold: games can be highly stochastic environments that allow presenting this task as a complex optimization problem and automatic play-testing, becoming benchmarks to advance the state of the art on AI methods. In this paper, we propose a general approach that employs the N-Tuple Bandit Evolutionary Algorithm (NTBEA) to tune parameters of three different games of the General Video Game AI (GVGAI) framework. The objective is to adjust the game experience of the players so the distribution of score events through the game approximates certain pre-defined target curves. We report satisfactory results for different target score trends and games, paving the path for future research in the area of automatically tuning player experience.

This paper describes the N-Tuple Bandit Evolutionary Algorithm (NTBEA) and its application to optimising the parameters of a rolling horizon evolution game-playing agent. The NTBEA combines evolutionary search with Multi-Armed Bandit algorithms (MABs) in order to provide an algorithm which is robust to noise, has an explicit way to balance the tradeoff between exploration and exploitation, and provides a statistical model of the fitness landscape as an additional output. The applications of this type of algorithm are numerous. In our research we have already applied it successfully to hyperparameter optimisation [1] and automated game tuning [2]. Furthermore, if the inherent fitness landscape is flat, then the exploration term provides a means for performing novelty search [3]. A. Estimation of Distribution Algorithms Estimation of Distribution Algorithms (EDAs) [4], [5], [6], [7], [8] are a powerful class of Evolutionary Algorithms (EAs).