  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.

Databases;Machine Learning