Deep reinforcement learning in applied control: Challenges, analysis, and insights

Deep reinforcement learning (DRL) algorithms have shown increasing promise for autonomous decision-making and continuous control, yet their practical value relative to established control methods remains difficult to assess under realistic control-engineering conditions. Existing evaluations are often narrow, application-specific, or based on inconsistent assumptions. As a result, it remains unclear when learned controllers are suitable alternatives to conventional designs. This paper presents a systematic assessment framework that compares four prominent DRL controllers with a classical control baseline across a diverse set of applied control problems, including non-minimum phase dynamics, flexible mechanical systems, nonlinear marine control, and aerial robotics. The framework standardises modelling assumptions, reward design, controller tuning, computational budgets, performance metrics, robustness tests, and deployment conditions. This allows fair and reproducible analysis across methods and applications. The evaluation covers tracking accuracy, settling time, overshoot, control effort, actuator saturation, disturbance rejection, model uncertainty, robustness margins, and sim-to-real transfer. The results show that performance depends strongly on the algorithmic structure, system dynamics, and operating conditions. Learned controllers provide advantages in some cases, particularly for nonlinearities, constraints, and uncertain operating regimes. Classical control remains competitive in accuracy, simplicity, and reliability for several benchmark conditions. Under combined saturation and unmodelled dynamics, the learned controllers maintained stable operation where the classical baseline became unstable. Real-time deployment on a quadrotor platform further demonstrated stable sim-to-real transfer. The study establishes a rigorous and reproducible evidence base for assessing the practical suitability of these data-driven control algorithms in applied control. It clarifies the trade-offs between learning-based and conventional control. This addresses a critical gap that cannot be easily inferred from isolated, system-specific studies.