  Although deep reinforcement learning has become a universal solution for
complex control tasks, its real-world applicability is still limited because
lacking security guarantees for policies. To address this problem, we propose
Boundary Characterization via the Minimum Experience Retention (BCMER), an
end-to-end Interpretable Policy Distillation (IPD) framework. Unlike previous
IPD approaches, BCMER distinguishes the importance of experiences and keeps a
minimal but critical experience pool with almost no loss of policy similarity.
Specifically, the proposed BCMER contains two basic steps. Firstly, we propose
a novel multidimensional hyperspheres intersection (MHI) approach to divide
experience points into boundary points and internal points, and reserve the
crucial boundary points. Secondly, we develop a nearest-neighbor-based model to
generate robust and interpretable decision rules based on the boundary points.
Extensive experiments show that the proposed BCMER is able to reduce the amount
of experience to 1.4%~19.1% (when the count of the naive experiences is 10k)
and maintain high IPD performance. In general, the proposed BCMER is more
suitable for the experience storage limited regime because it discovers the
critical experience and eliminates redundant experience.
