A Reinforcement Learning Behavior Tree Framework for Game AI

DOI

10.2991/essaeme-16.2016.120How to use a DOI?

Keywords

Behavior Tree; Reinforcement Learning; Game AI; Agent; Raven.

Abstract

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

Copyright

© 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/).

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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  -