212 research outputs found
Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication
We consider a cooperative multiplayer bandit learning problem where the
players are only allowed to agree on a strategy beforehand, but cannot
communicate during the learning process. In this problem, each player
simultaneously selects an action. Based on the actions selected by all players,
the team of players receives a reward. The actions of all the players are
commonly observed. However, each player receives a noisy version of the reward
which cannot be shared with other players. Since players receive potentially
different rewards, there is an asymmetry in the information used to select
their actions. In this paper, we provide an algorithm based on upper and lower
confidence bounds that the players can use to select their optimal actions
despite the asymmetry in the reward information. We show that this algorithm
can achieve logarithmic (gap-dependent)
regret as well as (gap-independent) regret. This is
asymptotically optimal in . We also show that it performs empirically better
than the current state of the art algorithm for this environment
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players
We study a multiplayer stochastic multi-armed bandit problem in which players
cannot communicate, and if two or more players pull the same arm, a collision
occurs and the involved players receive zero reward. We consider the
challenging heterogeneous setting, in which different arms may have different
means for different players, and propose a new and efficient algorithm that
combines the idea of leveraging forced collisions for implicit communication
and that of performing matching eliminations. We present a finite-time analysis
of our algorithm, giving the first sublinear minimax regret bound for this
problem, and prove that if the optimal assignment of players to arms is unique,
our algorithm attains the optimal regret, solving an open question
raised at NeurIPS 2018.Comment: AISTATS202
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits
Multi-player multi-armed bandit is an increasingly relevant decision-making
problem, motivated by applications to cognitive radio systems. Most research
for this problem focuses exclusively on the settings that players have
\textit{full access} to all arms and receive no reward when pulling the same
arm. Hence all players solve the same bandit problem with the goal of
maximizing their cumulative reward. However, these settings neglect several
important factors in many real-world applications, where players have
\textit{limited access} to \textit{a dynamic local subset of arms} (i.e., an
arm could sometimes be ``walking'' and not accessible to the player). To this
end, this paper proposes a \textit{multi-player multi-armed walking bandits}
model, aiming to address aforementioned modeling issues. The goal now is to
maximize the reward, however, players can only pull arms from the local subset
and only collect a full reward if no other players pull the same arm. We adopt
Upper Confidence Bound (UCB) to deal with the exploration-exploitation tradeoff
and employ distributed optimization techniques to properly handle collisions.
By carefully integrating these two techniques, we propose a decentralized
algorithm with near-optimal guarantee on the regret, and can be easily
implemented to obtain competitive empirical performance.Comment: AAAI 202
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