High operating speeds and use of aggressive fabrication technologies necessitate validation of mixed-signal electronic systems at every stage of top-down design: behavioral to netlist to physical design to silicon. At each step, design validation establishes the equivalence of lower level design descriptions against their higher level specifications. Prior research has leveraged state reachability analysis, nonconvex optimization, or performance specifications in order to generate tests. In contrast, we reformulate the systems under validation as a Markov decision process and examine the use of reinforcement-learning to provide a globally convergent solution, a means of “storing” the valuable information created during stimulus generation, and low-cost iterated generation. The integration of the proposed design validation methodology with deep-Q learning software and the suite of Cadence simulation tools is presented, validation results for selected design bugs in representative designs are analyzed, and the quality and efficiency of the proposed design validation methodology is discussed.