# Multi-Agent Dynamic Pricing in a Blockchain Protocol Using Gaussian Bandits

arXiv: https://arxiv.org/abs/2212.07942 · HTML: https://mlsanity.com/paper/2212.07942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 13 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Reinforcement Learning for Fair Dynamic Pricing](https://mlsanity.com/paper/1803.09967/) (2018)
- [Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experiment](https://mlsanity.com/paper/1912.02572/) (2019)
- [Dynamic Pricing in Competitive Markets](https://mlsanity.com/paper/1709.04960/) (2017)

## Abstract (arXiv)

The Graph Protocol indexes historical blockchain transaction data and makes it available for querying. As the protocol is decentralized, there are many independent Indexers that index and compete with each other for serving queries to the Consumers. One dimension along which Indexers compete is pricing. In this paper, we propose a bandit-based algorithm for maximization of Indexers' revenue via Consumer budget discovery. We present the design and the considerations we had to make for a dynamic pricing algorithm being used by multiple agents simultaneously. We discuss the results achieved by our dynamic pricing bandits both in simulation and deployed into production on one of the Indexers operating on Ethereum. We have open-sourced both the simulation framework and tools we created, which other Indexers have since started to adapt into their own workflows.