  Join order selection plays a significant role in query performance. However,
modern query optimizers typically employ static join enumeration algorithms
that do not receive any feedback about the quality of the resulting plan.
Hence, optimizers often repeatedly choose the same bad plan, as they do not
have a mechanism for "learning from their mistakes". In this paper, we argue
that existing deep reinforcement learning techniques can be applied to address
this challenge. These techniques, powered by artificial neural networks, can
automatically improve decision making by incorporating feedback from their
successes and failures. Towards this goal, we present ReJOIN, a
proof-of-concept join enumerator, and present preliminary results indicating
that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan
quality and join enumeration efficiency.
