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.},
}