GAIG Game AI Research Group @ QMUL

Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing


Abstract

For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game. We propose adapting such methods for Rolling Horizon Evolutionary Algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent’s win rate. On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach. We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.
Github: https://github.com/rdgain/ExperimentData/tree/RHEA-Online-Tuning-20 
YouTube: https://youtu.be/pTxSJHREpuc 

Cite this work

@inproceedings{gaina2020onlinerhea,
author= {Raluca D. Gaina and Chiara F. Sironi and Mark H.M. Winands and Diego Perez-Liebana and Simon M. Lucas},
title= {{Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing}},
year= {2020},
booktitle= {{IEEE Conference on Games (CoG)}},
abstract= {For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game. We propose adapting such methods for Rolling Horizon Evolutionary Algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent’s win rate. On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach. We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.},
}

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