A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

Abstract

One major cost of improving the automotive fuel economy while simultaneously reducing tailpipe emissions is increased powertrain complexity. This complexity has consequently increased the resources (both time and money) needed to develop such powertrains. Powertrain performance is heavily influenced by the quality of the controller/calibration. Since traditional control development processes are becoming resource-intensive, better alternate methods are worth pursuing. Recently, reinforcement learning (RL), a machine learning technique, has proven capable of creating optimal controllers for complex systems. The model-free nature of RL has the potential to streamline the control development process, possibly reducing the time and money required. This article reviews the impact of choices in two areas on the performance of RL-based powertrain controllers to provide a better awareness of their benefits and consequences. First, we examine how RL algorithm action continuities and control–actuator continuities are matched, via native operation or conversion. Secondly, we discuss the formulation of the reward function. RL is able to optimize control policies defined by a wide spectrum of reward functions, including some functions that are difficult to implement with other techniques. RL action and control–actuator continuity matching affects the ability of the RL-based controller to understand and operate the powertrain while the reward function defines optimal behavior. Finally, opportunities for future RL-based powertrain control development are identified and discussed.

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

“…Thus, the control policy could be improved continuously based on the measurement feedback. Additionally, several other physical nonlinearities and parasitic influences from other drive system components can be adequately captured in the reinforced-based learning environment [30,31]. Although the learning process can be performed offline, the trained RL agent can connect to a control interface in the controller making it suitable for real-time implementation without demanding a drastic change in the embedded hardware [32,33].…”