# Transfer in Sequential Multi-armed Bandits via Reward Samples

arXiv: https://arxiv.org/abs/2403.12428 · HTML: https://mlsanity.com/paper/2403.12428/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Multi-Task Learning for Contextual Bandits](https://mlsanity.com/paper/1705.08618/) (2017)
- [Meta-learning with Stochastic Linear Bandits](https://mlsanity.com/paper/2005.08531/) (2020)
- [Sequential Transfer in Multi-armed Bandit with Finite Set of Models](https://mlsanity.com/paper/1307.6887/) (2013)
- [Multitask Bandit Learning Through Heterogeneous Feedback Aggregation](https://mlsanity.com/paper/2010.15390/) (2020)
- [Meta-Learning for Simple Regret Minimization](https://mlsanity.com/paper/2202.12888/) (2022)

## Built on by (most cited first, 2 of 2)

- [Exploiting Adjacent Similarity in Multi-Armed Bandit Tasks via Transfer of Reward Samples](https://mlsanity.com/paper/2409.19975/) (2024)
- [Asymptotically Optimal Problem-Dependent Bandit Policies for Transfer Learning](https://mlsanity.com/paper/2509.19098/) (2025)

## Abstract (arXiv)

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.