Reinforcement learning in BitTorrent systems

Rafit Izhak-Ratzin, Hyunggon Park, Mihaela Van Der Schaar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


In this paper, we propose a BitTorrent-like protocol that replaces the peer selection mechanisms in the regular BitTorrent protocol with a novel reinforcement learning based mechanism. The inherent operation of P2P systems, which involves repeated interactions among peers over a long time period, allows peers to efficiently identify free-riders as well as desirable collaborators by learning the behavior of their associated peers. Thus, it can help peers improve their download rates and discourage free-riding (FR), while improving fairness. We model the peers' interactions in the BitTorrent-like network as a repeated interaction game, where we explicitly consider the strategic behavior of the peers. A peer that applies the reinforcement learning based mechanism uses a partial history of the observations on associated peers' statistical reciprocal behaviors to determine its best responses and estimate the corresponding impact on its expected utility. The policy determines the peer's resource reciprocations with other peers, which would maximize the peer's long-term performance.

Original languageEnglish
Title of host publication2011 Proceedings IEEE INFOCOM
Number of pages5
StatePublished - 2011
EventIEEE INFOCOM 2011 - Shanghai, China
Duration: 10 Apr 201115 Apr 2011

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


ConferenceIEEE INFOCOM 2011


  • BitTorrent
  • P2P
  • reinforcement learning


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