摘要
基于强化学习,设计了一个面向三维第一人称射击游戏(DOOM)的智能体,该智能体可在游戏环境下移动、射击敌人、收集物品等.本文算法结合深度学习的目标识别算法Faster RCNN与Deep Q-Networks(DQN)算法,可将DQN算法的搜索空间大大减小,从而极大提升本文算法的训练效率.在虚拟游戏平台(ViZDoom)的两个场景下(Defend_the_center和Health_gathering)进行实验,将本文算法与最新的三维射击游戏智能体算法进行比较,结果表明本文算法可以用更少的迭代次数实现更优的训练结果.
Based on reinforcement learning,an agent for three-dimensional first person shooting game(DOOM)was designed.The agent can move,shoot enemies and collect objects in the game environment.The proposed algorithm combines the Faster RCNN algorithm of deep learning and the Deep Q-Networks(DQN)algorithm of reinforcement learning,which can greatly reduce the search space of DQN algorithm and improve the training efficiency of the proposed algorithm.The experiments were carried out in two scenes(Defend_the_center and Health_gathering)of the virtual game platform(ViZDoom),and the proposed algorithm was compared with the state-of-the-art three-dimensional shooting game agent algorithm.The results show that the proposed algorithm can achieve better training results with fewer iterations.
作者
孟琭
沈凝
祁殷俏
张昊园
MENG Lu;SHEN Ning;QI Yin-qiao;ZHANG Hao-yuan(School of Information Science&Engineering,Northeastern University,Shenyang 110819,China)
出处
《东北大学学报(自然科学版)》
EI
CAS
CSCD
北大核心
2021年第4期478-482,493,共6页
Journal of Northeastern University(Natural Science)
基金
国家重点研发计划项目(2018YFB2003502)
国家自然科学基金资助项目(62073061)
中央高校基本科研业务费专项资金资助项目(N2004020).