[2405.01440] A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving

Computer Science > Robotics arXiv:2405.01440 (cs) [Submitted on 12 Apr 2024 (v1), last revised 4 Mar 2026 (this version, v3)] Title:A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving Authors:Ahmed Abouelazm, Jonas Michel, J. Marius Zoellner View a PDF of the paper titled A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving, by Ahmed Abouelazm and 2 other authors View PDF HTML (experimental) Abstract:Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This paper aims to highlight the gap in such function design by assessing different proposed formulations in the literature and dividing individual objectives into Safety, Comfort, Progress, and Traffic Rules compliance categories. Additionally, the limitations of the reviewed reward functions are discussed, such as objectives aggregation and indifference to driving context. Furthermore, the reward categories are frequently inadequately formulated and lack standardization. This paper concludes by proposing future research that potentially addresses the observed shortcomings in rewards, includ...