In-context Learning for Automated Driving Scenarios

1 code implementation • 7 May 2024 • Ziqi Zhou, Jingyue Zhang, Jingyuan Zhang, Boyue Wang, Tianyu Shi, Alaa Khamis

One of the key challenges in current Reinforcement Learning (RL)-based Automated Driving (AD) agents is achieving flexible, precise, and human-like behavior cost-effectively.

In-Context Learning Reinforcement Learning (RL)