A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving

Published in arXiv preprint arXiv:2110.11573., 2022

Recommended citation: Wang, G., Niu, H., Zhu, D., Hu, J., Zhan, X., and Zhou, G. A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving. arXiv preprint arXiv:2110.11573.


Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the fully end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. As a way of marrying the advantages of both approaches, learning a semantically meaningful representation and then use in the downstream driving policy learning tasks provides a viable and attractive solution. However, several key challenges remain to be addressed, including identifying the most effective representation, alleviating the sim-to-real generalization issue as well as balancing model training cost. In this study, we propose a versatile and efficient reinforcement learning framework and build a fully functional autonomous vehicle for real-world validation. Our framework shows great generalizability to various complicated real-world scenarios and superior training efficiency against the competing baselines.

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