Huang Hongye
Background: Autonomy needs an intelligent interplay of controllers involving subsystems such as steering, braking and suspension to maintain safety, comfort, and efficiency. Rule-based controllers and single-agent reinforcement learning have yet to prove scalable, adaptable, and robust within a dynamical multi-agent environment. Methods: This paper proposes a vertical collaboration framework, tying MADDPG methods with dSPACE real-time simulation for chassis domain control. The subsystems are seen as decentralized actors with a centralized critic, operating as independent agents, to permit adaptability at the subsystem level and cooperative global optimization. The framework includes variable noise for exploration trade-off, prioritized replay to speed up learning and HIL testing in deployment. Results: As a simulated task, a success rate of 93% was ideal, and targets were not deviated from much (0.16–0.17[Formula: see text]m), and an average passing time of (approximately) 18.5[Formula: see text]s was optimal. The model however adjusts the comfort parameter that the brake-and-steer system offers by setting the reward of an episode constant to all the agents. The applied model is more noise-resistant and reactive, as well as less complex as compared to the classical methods. Conclusions: The MADDPG-dSPACE framework aims at balancing the reinforcement learning theory with real-life practice by offering a scalable, modular, and robust framework to future autonomous driving systems that would ensure safety, adaptability, and cooperative chassis control by providing a broad range of driving scenarios.