LAPSE:2023.34889
Published Article
LAPSE:2023.34889
A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation
April 28, 2023
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.
Record ID
Keywords
powertrain control, reinforcement learning, review
Subject
Suggested Citation
Egan D, Zhu Q, Prucka R. A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation. (2023). LAPSE:2023.34889
Author Affiliations
Journal Name
Energies
Volume
16
Issue
8
First Page
3450
Year
2023
Publication Date
2023-04-14
Published Version
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en16083450, Publication Type: Review
Record Map
Published Article
LAPSE:2023.34889
This Record
External Link
doi:10.3390/en16083450
Publisher Version
Download
Meta
Record Statistics
Record Views
77
Version History
[v1] (Original Submission)
Apr 28, 2023
Verified by curator on
Apr 28, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.34889
Original Submitter
Auto Uploader for LAPSE
Links to Related Works