# Generalised correlated batched bandits via the ARC algorithm with application to dynamic pricing

arXiv: https://arxiv.org/abs/2102.04263 · HTML: https://mlsanity.com/paper/2102.04263/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Learning to Optimize via Information-Directed Sampling](https://mlsanity.com/paper/1403.5556/) (2014)
- [Information Directed Sampling and Bandits with Heteroscedastic Noise](https://mlsanity.com/paper/1801.09667/) (2018)
- [Asymptotic Randomised Control with applications to bandits](https://mlsanity.com/paper/2010.07252/) (2020)

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

- [Multi-Task Dynamic Pricing in Credit Market with Contextual Information](https://mlsanity.com/paper/2410.14839/) (2024)
- [ε-Policy Gradient for Online Pricing](https://mlsanity.com/paper/2405.03624/) (2024)

## Abstract (arXiv)

The Asymptotic Randomised Control (ARC) algorithm provides a rigorous approximation to the optimal strategy for a wide class of Bayesian bandits, while retaining low computational complexity. In particular, the ARC approach provides nearly optimal choices even when the payoffs are correlated or more than the reward is observed. The algorithm is guaranteed to asymptotically optimise the expected discounted payoff, with error depending on the initial uncertainty of the bandit. In this paper, we extend the ARC framework to consider a batched bandit problem where observations arrive from a generalised linear model. In particular, we develop a large sample approximation to allow correlated and generally distributed observation. We apply this to a classic dynamic pricing problem based on a Bayesian hierarchical model and demonstrate that the ARC algorithm outperforms alternative approaches.