Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Game of Thrones: Fully Distributed Learning for Multi-Player Bandits   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-O\left(\log T\right). 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.

22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an