The absence of collision information in Multi- player Multi-armed bandits (MMABs) renders arm availabilities partially observable, impeding the design of algorithms with regret guarantees that do not allow inter-player communication. In this work, we propose a collision resolution (CR) mechanism for MMABs inspired from sequential interference mechanisms employed in communication protocols. In the general case, our collision resolution mechanism assumes that players can pull multiple arms during the exploration phase. We, thus, propose a novel MMAB model that captures this while still considering strictly bandit feedback and single-pulls during the exploitation phase. We theoretically analyze the CR mechanism using tools from information the...
We consider the problem of multiple users targeting the arms of a single multi-armed stochastic band...
We consider the problem of distributed online learning with multiple players in multi-armed bandits ...
International audienceWe investigate a nonstochastic bandit setting in which the loss of an action i...
We study the problem of information sharing and cooperation in Multi-Player Multi-Armed bandits. We ...
International audienceWe propose a novel algorithm for multi-player multi-armed bandits without coll...
We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, an...
International audienceMotivated by cognitive radio networks, we consider the stochastic multiplayer ...
We consider a setting where multiple players sequentially choose among a common set of actions (arms...
We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to p...
Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by ap...
The uncoordinated spectrum access problem is studied using a multi-player multi-armed bandits framew...
International audienceMulti-player Multi-Armed Bandits (MAB) have been extensively studied in the li...
Multi-armed bandits (MAB) have attracted much attention as a means of capturing the exploration and ...
works released after June 2022 are not considered in this surveyDue mostly to its application to cog...
International audienceMotivated by cognitive radios, stochastic multi-player multi-armed bandits gai...
We consider the problem of multiple users targeting the arms of a single multi-armed stochastic band...
We consider the problem of distributed online learning with multiple players in multi-armed bandits ...
International audienceWe investigate a nonstochastic bandit setting in which the loss of an action i...
We study the problem of information sharing and cooperation in Multi-Player Multi-Armed bandits. We ...
International audienceWe propose a novel algorithm for multi-player multi-armed bandits without coll...
We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, an...
International audienceMotivated by cognitive radio networks, we consider the stochastic multiplayer ...
We consider a setting where multiple players sequentially choose among a common set of actions (arms...
We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to p...
Multi-player multi-armed bandit is an increasingly relevant decision-making problem, motivated by ap...
The uncoordinated spectrum access problem is studied using a multi-player multi-armed bandits framew...
International audienceMulti-player Multi-Armed Bandits (MAB) have been extensively studied in the li...
Multi-armed bandits (MAB) have attracted much attention as a means of capturing the exploration and ...
works released after June 2022 are not considered in this surveyDue mostly to its application to cog...
International audienceMotivated by cognitive radios, stochastic multi-player multi-armed bandits gai...
We consider the problem of multiple users targeting the arms of a single multi-armed stochastic band...
We consider the problem of distributed online learning with multiple players in multi-armed bandits ...
International audienceWe investigate a nonstochastic bandit setting in which the loss of an action i...