  Recently deep reinforcement learning has achieved tremendous success in wide
ranges of applications. However, it notoriously lacks data-efficiency and
interpretability. Data-efficiency is important as interacting with the
environment is expensive. Further, interpretability can increase the
transparency of the black-box-style deep RL models and hence gain trust from
the users. In this work, we propose a new hierarchical framework via symbolic
RL, leveraging a symbolic transition model to improve the data-efficiency and
introduce the interpretability for learned policy. This framework consists of a
high-level agent, a subtask solver and a symbolic transition model. Without
assuming any prior knowledge on the state transition, we adopt inductive logic
programming (ILP) to learn the rules of symbolic state transitions, introducing
interpretability and making the learned behavior understandable to users. In
empirical experiments, we confirmed that the proposed framework offers
approximately between 30\% to 40\% more data efficiency over previous methods.
