Faster and Safer Training by Embedding High-Level Knowledge into Deep
  Reinforcement Learning
  Deep reinforcement learning has been successfully used in many dynamic
decision making domains, especially those with very large state spaces.
However, it is also well-known that deep reinforcement learning can be very
slow and resource intensive. The resulting system is often brittle and
difficult to explain. In this paper, we attempt to address some of these
problems by proposing a framework of Rule-interposing Learning (RIL) that
embeds high level rules into the deep reinforcement learning. With some good
rules, this framework not only can accelerate the learning process, but also
keep it away from catastrophic explorations, thus making the system relatively
stable even during the very early stage of training. Moreover, given the rules
are high level and easy to interpret, they can be easily maintained, updated
and shared with other similar tasks.
