  In this article, we propose a novel algorithm for deep reinforcement learning
named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning
and aims at incorporating semi-supervised learning into reinforcement learning
through splitting Q-values into state values and action advantages. We require
that an offline expert assesses the value of a state in a coarse manner using
three discrete values. An expert network is designed in addition to the
Q-network, which updates each time following the regular offline minibatch
update whenever the expert example buffer is not empty. Using the board game
Othello, we compare our algorithm with the baseline Q-learning algorithm, which
is a combination of Double Q-learning and Dueling Q-learning. Our results show
that Expert Q-learning is indeed useful and more resistant to the
overestimation bias. The baseline Q-learning algorithm exhibits unstable and
suboptimal behavior in non-deterministic settings, whereas Expert Q-learning
demonstrates more robust performance with higher scores, illustrating that our
algorithm is indeed suitable to integrate state values from expert examples
into Q-learning.
