  Deep reinforcement learning is quickly changing the field of artificial
intelligence. These models are able to capture a high level understanding of
their environment, enabling them to learn difficult dynamic tasks in a variety
of domains. In the database field, query optimization remains a difficult
problem. Our goal in this work is to explore the capabilities of deep
reinforcement learning in the context of query optimization. At each state, we
build queries incrementally and encode properties of subqueries through a
learned representation. The challenge here lies in the formation of the state
transition function, which defines how the current subquery state combines with
the next query operation (action) to yield the next state. As a first step in
this direction, we focus the state representation problem and the formation of
the state transition function. We describe our approach and show preliminary
results. We further discuss how we can use the state representation to improve
query optimization using reinforcement learning.
