A proposal of privacy preserving reinforcement learning for secure multiparty computation

Hirofumi Miyajima, Noritaka Shigei, Syunki Makino, Hiromi Miyajima, Yohtaro Miyanishi, Shinji Kitagami, Norio Shiratori


Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.

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DOI: https://doi.org/10.5430/air.v6n2p57


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Artificial Intelligence Research

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