Gambler Bandits and the Regret of Being Ruined

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

Publikation: KonferenzbeitragPaperBegutachtung


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.
PublikationsstatusEingereicht - 17 März 2021
Veranstaltung20th International Conference on Autonomous Agents and Multiagent Systems : International Foundation for Autonomous Agents and MultiagentSystems (IFAAMAS) - fully virtual event, London
Dauer: 3 Mai 20217 Mai 2021
Konferenznummer: hal-03120813


Konferenz20th International Conference on Autonomous Agents and Multiagent Systems
KurztitelAAMAS 2021

Dieses zitieren