  Current deep reinforcement learning (DRL) algorithms utilize randomness in
simulation environments to assume complete coverage in the state space.
However, particularly in high dimensions, relying on randomness may lead to
gaps in coverage of the trained DRL neural network model, which in turn may
lead to drastic and often fatal real-world situations. To the best of the
author's knowledge, the assessment of coverage for DRL is lacking in current
research literature. Therefore, in this paper, a novel measure, Approximate
Pseudo-Coverage (APC), is proposed for assessing the coverage in DRL
applications. We propose to calculate APC by projecting the high dimensional
state space on to a lower dimensional manifold and quantifying the occupied
space. Furthermore, we utilize an exploration-exploitation strategy for
coverage maximization using Rapidly-Exploring Random Tree (RRT). The efficacy
of the assessment and the acceleration of coverage is demonstrated on standard
tasks such as Cartpole, highway-env.
