  Machine learning and formal methods have complimentary benefits and
drawbacks. In this work, we address the controller-design problem with a
combination of techniques from both fields. The use of black-box neural
networks in deep reinforcement learning (deep RL) poses a challenge for such a
combination. Instead of reasoning formally about the output of deep RL, which
we call the {\em wizard}, we extract from it a decision-tree based model, which
we refer to as the {\em magic book}. Using the extracted model as an
intermediary, we are able to handle problems that are infeasible for either
deep RL or formal methods by themselves. First, we suggest, for the first time,
combining a magic book in a synthesis procedure. We synthesize a stand-alone
correct-by-design controller that enjoys the favorable performance of RL.
Second, we incorporate a magic book in a bounded model checking (BMC)
procedure. BMC allows us to find numerous traces of the plant under the control
of the wizard, which a user can use to increase the trustworthiness of the
wizard and direct further training.
