976 research outputs found
Decentralized Learning for Multi-player Multi-armed Bandits
We consider the problem of distributed online learning with multiple players
in multi-armed bandits (MAB) models. Each player can pick among multiple arms.
When a player picks an arm, it gets a reward. We consider both i.i.d. reward
model and Markovian reward model. In the i.i.d. model each arm is modelled as
an i.i.d. process with an unknown distribution with an unknown mean. In the
Markovian model, each arm is modelled as a finite, irreducible, aperiodic and
reversible Markov chain with an unknown probability transition matrix and
stationary distribution. The arms give different rewards to different players.
If two players pick the same arm, there is a "collision", and neither of them
get any reward. There is no dedicated control channel for coordination or
communication among the players. Any other communication between the users is
costly and will add to the regret. We propose an online index-based distributed
learning policy called algorithm that trades off
\textit{exploration v. exploitation} in the right way, and achieves expected
regret that grows at most as near-. The motivation comes from
opportunistic spectrum access by multiple secondary users in cognitive radio
networks wherein they must pick among various wireless channels that look
different to different users. This is the first distributed learning algorithm
for multi-player MABs to the best of our knowledge.Comment: 33 pages, 3 figures. Submitted to IEEE Transactions on Information
Theor
Concurrent bandits and cognitive radio networks
We consider the problem of multiple users targeting the arms of a single
multi-armed stochastic bandit. The motivation for this problem comes from
cognitive radio networks, where selfish users need to coexist without any side
communication between them, implicit cooperation or common control. Even the
number of users may be unknown and can vary as users join or leave the network.
We propose an algorithm that combines an -greedy learning rule with a
collision avoidance mechanism. We analyze its regret with respect to the
system-wide optimum and show that sub-linear regret can be obtained in this
setting. Experiments show dramatic improvement compared to other algorithms for
this setting
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