Rolling Horizon Evolutionary Algorithms for General Video Game Playing

by   Raluca D. Gaina, et al.
Queen Mary University of London

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.


page 1

page 7

page 8

page 9


Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing

Monte Carlo Tree Search techniques have generally dominated General Vide...

Population Seeding Techniques for Rolling Horizon Evolution in General Video Game Playing

While Monte Carlo Tree Search and closely related methods have dominated...

Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

This paper introduces a simple and fast variant of Planet Wars as a test...

Rolling Horizon NEAT for General Video Game Playing

This paper presents a new Statistical Forward Planning (SFP) method, Rol...

Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic

Competitive board games have provided a rich and diverse testbed for art...

An experimental study of exhaustive solutions for the Mastermind puzzle

Mastermind is in essence a search problem in which a string of symbols t...

Statistical Tree-based Population Seeding for Rolling Horizon EAs in General Video Game Playing

Multiple Artificial Intelligence (AI) methods have been proposed over re...

Please sign up or login with your details

Forgot password? Click here to reset