CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
Deep reinforcement learning with Experience Replay based on SARSA
Zhao,Dongbin(赵冬斌); Wang,Haitao; Shao,Kun; Zhu,Yuanheng
Conference NameProceedings of IEEE Symposium Series on Computational Intelligence (SSCI 2016) – Symposium on Adaptive Dynamic Programming and Reinforcement Learning
Conference Date2016-9
Conference Place*
AbstractSARSA, as one kind of on-policy reinforcement learning methods, is integrated with deep learning to solve the video games control problems in this paper. We use deep convolutional neural network to estimate the state-action value, and SARSA learning to update it. Besides, experience replay is introduced to make the training process suitable to scalable machine learning problems. In this way, a new deep reinforcement learning method, called deep SARSA is proposed to solve complicated control problems such as imitating human to play video games. From the experiments results, we can conclude that the deep SARSA learning shows better performances in some aspects than deep Q learning.
KeywordDeep Learning Reinforcement Learning Experience Replay q Learning Sarsa Learning
Document Type会议论文
AffiliationKey Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences, Beijing 100190, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhao,Dongbin,Wang,Haitao,Shao,Kun,et al. Deep reinforcement learning with Experience Replay based on SARSA[C],2016.
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