Multi-Agent Dynamic Pricing in a Blockchain Protocol Using Gaussian Bandits
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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.
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References · 6
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
- 2017Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learningcited by 114
- 2018Reinforcement Learning for Fair Dynamic Pricingcited by 9
- 2019Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experimentcited by 6
- 2017Dynamic Pricing in Competitive Marketscited by 1
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