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- W2952233008 abstract "We consider an N-player multi-armed bandit game where each player chooses one out of M arms for T turns. Each player has different expected rewards for the arms, and the instantaneous rewards are independent and identically distributed or Markovian. When two or more players choose the same arm, they all receive zero reward. Performance is measured using the expected sum of regrets, compared with an optimal assignment of arms to players that maximizes the sum of expected rewards. We assume that each player only knows her actions and the reward she received each turn. Players cannot observe the actions of other players, and no communication between players is possible. We present a distributed algorithm and prove that it achieves an expected sum of regrets of near-Oleft(log Tright). This is the first algorithm to achieve a near order optimal regret in this fully distributed scenario. All other works have assumed that either all players have the same vector of expected rewards or that communication between players is possible." @default.
- W2952233008 created "2019-06-27" @default.
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- W2952233008 date "2018-10-26" @default.
- W2952233008 modified "2023-09-26" @default.
- W2952233008 title "Distributed Multi-Player Bandits - a Game of Thrones Approach" @default.
- W2952233008 hasPublicationYear "2018" @default.
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