Transfer in Sequential Multi-armed Bandits via Reward Samples

We consider a sequential stochastic multi-armed bandit problem where the agent interacts with bandit over multiple episodes. The reward distribution of the arms remain constant throughout an episode but can change over different episodes. We propose an algorithm based on UCB to transfer the reward s...

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Bibliographic Details
Published in:arXiv.org 2024-03
Main Authors: Rahul, N R, Katewa, Vaibhav
Format: Article
Language:English
Subjects:
Algorithms
Empirical analysis
Multi-armed bandit problems
Online Access:Get full text
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Summary:We consider a sequential stochastic multi-armed bandit problem where the agent interacts with bandit over multiple episodes. The reward distribution of the arms remain constant throughout an episode but can change over different episodes. We propose an algorithm based on UCB to transfer the reward samples from the previous episodes and improve the cumulative regret performance over all the episodes. We provide regret analysis and empirical results for our algorithm, which show significant improvement over the standard UCB algorithm without transfer.
ISSN:2331-8422