A Reinforcement Learning Behavior Tree Framework for Game AI
Yanchang Fu, Long Qin, Quanjun Yin
Available Online August 2016.
- 10.2991/essaeme-16.2016.120How to use a DOI?
- Behavior Tree; Reinforcement Learning; Game AI; Agent; Raven.
This paper discussed the implementation of behavior tree technology in behavioral modeling domain. Existing framework can't provide the ability of reasoning while take into account the ability of learning. To solve this problem, we propose a reinforcement learning behavior tree framework based on reinforcement theory. Following our study, a QBot model is build based on the framework in the Raven platform, a popular test bed for game AI development. This paper carried out simulation experiments which include 3 opponent agents. The result shows that QBot outperforms the other 2 Raven_Bots which adopt the default agent model in Raven platform, and thus the result proves that the framework is advanced
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yanchang Fu AU - Long Qin AU - Quanjun Yin PY - 2016/08 DA - 2016/08 TI - A Reinforcement Learning Behavior Tree Framework for Game AI BT - Proceedings of the 2016 International Conference on Economics, Social Science, Arts, Education and Management Engineering PB - Atlantis Press SP - 573 EP - 579 SN - 2352-5398 UR - https://doi.org/10.2991/essaeme-16.2016.120 DO - 10.2991/essaeme-16.2016.120 ID - Fu2016/08 ER -