Privacy-preserving reinforcement learning

Jun Sakuma, Shigenobu Kobayashi, Rebecca N. Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


We consider the problem of distributed reinforcement learning (DRL) from private perceptions. In our setting, agents' perceptions, such as states, rewards, and actions, are not only distributed but also should be kept private. Conventional DRL algorithms can handle multiple agents, but do not necessarily guarantee privacy preservation and may not guarantee optimality. In this work, we design cryptographic solutions that achieve optimal policies without requiring the agents to share their private information.

Original languageEnglish (US)
Title of host publicationProceedings of the 25th International Conference on Machine Learning
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)9781605582054
StatePublished - 2008
Event25th International Conference on Machine Learning - Helsinki, Finland
Duration: Jul 5 2008Jul 9 2008

Publication series

NameProceedings of the 25th International Conference on Machine Learning


Other25th International Conference on Machine Learning

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Software


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