# Multi-agent Databases via Independent Learning

arXiv: https://arxiv.org/abs/2205.14323 · HTML: https://mlsanity.com/paper/2205.14323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 28 May 2022.

## Builds on (most cited first, 16 of 16)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Neo: A Learned Query Optimizer](https://mlsanity.com/paper/1904.03711/) (2019)
- [Learned Cardinalities: Estimating Correlated Joins with Deep Learning](https://mlsanity.com/paper/1809.00677/) (2018)
- [An End-to-End Learning-based Cost Estimator](https://mlsanity.com/paper/1906.02560/) (2019)
- [Deep Unsupervised Cardinality Estimation](https://mlsanity.com/paper/1905.04278/) (2019)
- [Learning to Optimize Join Queries With Deep Reinforcement Learning](https://mlsanity.com/paper/1808.03196/) (2018)
- [Database Meets Deep Learning: Challenges and Opportunities](https://mlsanity.com/paper/1906.08986/) (2019)
- [Benchmarking Learned Indexes](https://mlsanity.com/paper/2006.12804/) (2020)
- [Deep Reinforcement Learning for Join Order Enumeration](https://mlsanity.com/paper/1803.00055/) (2018)
- [SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning](https://mlsanity.com/paper/1901.05152/) (2019)
- [Learning State Representations for Query Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/1803.08604/) (2018)
- [LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations](https://mlsanity.com/paper/1808.07903/) (2018)
- [Towards a Hands-Free Query Optimizer through Deep Learning](https://mlsanity.com/paper/1809.10212/) (2018)
- [Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning](https://mlsanity.com/paper/2007.10568/) (2020)

## Abstract (arXiv)

Machine learning is rapidly being used in database research to improve the effectiveness of numerous tasks included but not limited to query optimization, workload scheduling, physical design, etc. Currently, the research focus has been on replacing a single database component responsible for one task by its learning-based counterpart. However, query performance is not simply determined by the performance of a single component, but by the cooperation of multiple ones. As such, learning based database components need to collaborate during both training and execution in order to develop policies that meet end performance goals. Thus, the paper attempts to address the question "Is it possible to design a database consisting of various learned components that cooperatively work to improve end-to-end query latency?". To answer this question, we introduce MADB (Multi-Agent DB), a proof-of-concept system that incorporates a learned query scheduler and a learned query optimizer. MADB leverages a cooperative multi-agent reinforcement learning approach that allows the two components to exchange the context of their decisions with each other and collaboratively work towards reducing the query latency. Preliminary results demonstrate that MADB can outperform the non-cooperative integration of learned components.