  Deep Reinforcement Learning (DRL) has achieved impressive performance in
robotics and autonomous systems (RAS). A key challenge to its deployment in
real-life operations is the presence of spuriously unsafe DRL policies.
Unexplored states may lead the agent to make wrong decisions that could result
in hazards, especially in applications where DRL-trained end-to-end controllers
govern the behaviour of RAS. This paper proposes a novel quantitative
reliability assessment framework for DRL-controlled RAS, leveraging
verification evidence generated from formal reliability analysis of neural
networks. A two-level verification framework is introduced to check the safety
property with respect to inaccurate observations that are due to, e.g.,
environmental noise and state changes. Reachability verification tools are
leveraged locally to generate safety evidence of trajectories. In contrast, at
the global level, we quantify the overall reliability as an aggregated metric
of local safety evidence, corresponding to a set of distinct tasks and their
occurrence probabilities. The effectiveness of the proposed verification
framework is demonstrated and validated via experiments on real RAS.
