Reinforcement Learning for Real-Time Control Using Quanser Platforms: A Structured Narrative Review

Abstract

Reinforcement learning (RL) is increasingly used for real-time control of complex dynamical systems, but its practical performance must be evaluated under hardware constraints that are often simplified in simulation. This paper presents a comprehensive review of published RL-based control studies using the Quanser Aero, Aero 2, 3-DOF Helicopter, and Autonomous Vehicles Research Studio (AVRS) platforms. The reviewed studies are compared according to the RL algorithm, control objective, hardware configuration, implementation environment, and reported experimental performance. The synthesis shows that Aero and Aero 2 are used primarily for stabilisation, trajectory tracking, and energy-aware control, whereas the 3-DOF Helicopter provides a more demanding benchmark for adaptive and Actor–Critic methods under nonlinear and coupled dynamics. AVRS offers significant potential for vision-based and multi-agent RL; however, the available experimental literature remains limited. Across the reviewed comparisons, policy-gradient and Actor–Critic methods, including PPO and SAC, generally demonstrate greater adaptability and smoother continuous-control behaviour, while conventional controllers frequently retain advantages in steady-state accuracy, computational predictability, and safety verification. Nevertheless, no RL algorithm can be identified as universally superior because the published studies employ heterogeneous reward functions, reference trajectories, sampling rates, performance measures, and hardware configurations. Recurring limitations include sample inefficiency, simulation-to-hardware discrepancies, computational latency, safety constraints, and incomplete reporting of experimental protocols. The review therefore identifies standardised evaluation procedures, reproducible reporting, safety-aware RL, interoperable software interfaces, and higher-fidelity digital twins as priorities for future research. No new experimental data are generated; the contribution is a comparative synthesis of experimental evidence reported in the literature.