摘要
空战行为决策的智能性是学术界关注的重要问题之一。提出一种基于Q-学习和行为树的CGF空战行为决策方法。通过构建CGF空战行为树模型,实现CGF智能行为;通过在行为树上的Q-学习,使CGF具有不断进化的能力。仿真结果表明,该算法在与传统算法对抗中,性能优势明显且学习能力较强。
The intelligence of air bat strategies is one of the important problems. A new method for air bat strategies of CGF was proposed based on Q-learning and behavior tree. The intelligence of CGF was formed through establishing behavior tree. And through Q-learning on behavior tree, the evolutionary ability was gained for CGF. Simulation shows that the method performs bet- ter and with a stronger learning ability when it combats with traditional algorithm.
出处
《计算机与现代化》
2017年第5期37-39,44,共4页
Computer and Modernization
基金
国家自然科学基金重大研究计划(91538201)
泰山学者专项基金资助项目(ts201511020)
关键词
空战决策
人工智能
行为树
Q-学习
air bat strategies
artificial intelligence
behavior tree
Q-learning
