Gambler Bandits and the Regret of Being Ruined

Filipo Perotto, Sattar Vakili, Pratik Gajane, Yaser Faghan, Mathieu Bourgais

Research output: Contribution to conferencePaperpeer-review


In this paper we consider a particular class of problems called
multiarmed gambler bandits (MAGB) which constitutes a modified
version of the Bernoulli MAB problem where two new elements
must be taken into account: the budget and the risk of ruin. The agent has an initial budget that evolves in time following the received rewards, which can be either +1 after a success or −1 after a failure. The problem can also be seen as a MAB version of the classic gambler’s ruin game. The contribution of this paper is a preliminary analysis on the probability of being ruined given the current budget and observations, and the proposition of an alternative regret formulation, combining the classic regret notion with the expected loss due to the probability of being ruined. Finally, standard state-of-the-art methods are experimentally compared using the proposed metric.
Original languageEnglish
Publication statusSubmitted - 17 Mar 2021
Event20th International Conference on Autonomous Agents and Multiagent Systems : International Foundation for Autonomous Agents and MultiagentSystems (IFAAMAS) - fully virtual event, London
Duration: 3 May 20217 May 2021
Conference number: hal-03120813


Conference20th International Conference on Autonomous Agents and Multiagent Systems
Abbreviated titleAAMAS 2021
Internet address


  • Peer-reviewed

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