Self-Adaptive Rolling Horizon Evolutionary Algorithms for General Video Game Playing
| Autor: | Raluca D. Gaina, Simon M. Lucas, Diego Perez-Liebana, Mark H. M. Winands, Chiara F. Sironi |
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| Přispěvatelé: | Dept. of Advanced Computing Sciences, RS: FSE DACS |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | CoG 2020 IEEE Conference on Games (CoG), 367-374 STARTPAGE=367;ENDPAGE=374;TITLE=2020 IEEE Conference on Games (CoG) |
| DOI: | 10.1109/CoG47356.2020.9231587 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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