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

Understand

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.