深度强化学习在Atari视频游戏上的应用  

The Application of Depth of reinforcement Learning in the Vedio Game

作　　者：石征锦[1] 王康[1] 

机构地区：[1]沈阳理工大学自动化与电气工程学院

出　　处：《电子世界》2017年第16期105-106,109,共3页Electronics World

摘　　要：考虑到深度学习在图像特征提取上的优势,为了提高深度学习在Atari游戏上的稳定性,在卷积神经网络和强化学习改进的Q-learning算法相结合的基础上,提出了一种基于模型融合的深度神经网络结构。实验表明,新的模型能够充分学习到控制策略,并且在Atari游戏上达到或者超出普通深度强化学习模型的得分,验证了模型融合的深度强化学习在视频游戏上的稳定性和优越性。Considering the advantage of depth learning in image feature extraction, In order to improve the depth study on the Atari game per- formance this paper proposes a depth neural network structure based on model fusion, convolution neural network and modified Q-learning algo- rithm.Experiments show that the new model can fully study the control strategy, and it achieve or exceed the scores of the general learning model in the Atari game.Proving the deep reinforcement learning based on model fusion have the stability and superiority in the video game.

关 键 词：强化学习 深度学习 神经网络 视频游戏 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]