Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning
Paper i proceeding, 2021

Making decisions in complex driving environments is a challenging task for autonomous agents. Imitation learning methods have great potentials for achieving such a goal. Adversarial Inverse Reinforcement Learning (AIRL) is one of the state-of-art imitation learning methods that can learn both a behavioral policy and a reward function simultaneously, yet it is only demonstrated in simple and static environments where no interactions are introduced. In this paper, we improve and stabilize AIRL's performance by augmenting it with semantic rewards in the learning framework. Additionally, we adapt the augmented AIRL to a more practical and challenging decision-making task in a highly interactive environment in autonomous driving. The proposed method is compared with four baselines and evaluated by four performance metrics. Simulation results show that the augmented AIRL outperforms all the baseline methods, and its performance is comparable with that of the experts on all of the four metrics.

Autonomous driving

Lane change

Inverse reinforcement learning

Decision making


Pin Wang

University of California

Dapeng Liu

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

Jiayu Chen

Beijing University of Technology

Hanhan Li

Google Inc.

Ching Yao Chan

University of California

Proceedings - IEEE International Conference on Robotics and Automation

10504729 (ISSN)

Vol. 2021-May 1036-1042
9781728190778 (ISBN)

2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Xi'an, China,



Robotteknik och automation

Datavetenskap (datalogi)



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