|Deep reinforcement learning with Experience Replay based on SARSA|
|Zhao,Dongbin（赵冬斌）; Wang,Haitao; Shao,Kun; Zhu,Yuanheng
|Conference Name||Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI 2016) – Symposium on Adaptive Dynamic Programming and Reinforcement Learning
|Abstract||SARSA, 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.|
|Affiliation||Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences, Beijing 100190, China|
Zhao,Dongbin,Wang,Haitao,Shao,Kun,et al. Deep reinforcement learning with Experience Replay based on SARSA[C],2016.
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