An Application of Reinforcement Learning to the Optimal Design of Road Vehicle Suspension Systems

This study investigates the application of Multi-Objective Reinforcement Learning–Dominance-Based (MORL–DB) method to the optimal design of complex mechanical systems. The MORL–DB method employs a Deep Deterministic Policy Gradient (DDPG) agent to identify the optimal solutions of the multi-objective problem. By adopting the k-optimality metric, which introduces an optimality ranking within the Pareto-optimal set of solutions, a final design solution can be chosen more easily, especially when considering a large number of objective functions. The method is successfully applied to the elasto-kinematic optimisation of a double wishbone suspension system, featuring a multi-body model in ADAMS Car. This complex design task includes 30 design variables and 14 objective functions. The MORL–DB method is compared with two other approaches: the Moving Spheres (MS) method, specifically developed for spatial design tasks, and the genetic algorithm with k-optimality-based sorting (KEMOGA). Comparative results show that the MORL–DB method achieves solutions of higher optimality while requiring significantly fewer objective function evaluations. The results demonstrate that the MORL–DB method is a promising and sample-efficient alternative for multi-objective optimisation, particularly in problems involving high-dimensional design spaces and expensive objective function evaluations.