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arXiv · 1911.03799

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning

Zhiqian Qiao·Zachariah Tyree·Priyantha Mudalige·Jeff Schneider·John M. Dolan
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Abstract

In this work, we propose a hierarchical reinforcement learning (HRL) structure which is capable of performing autonomous vehicle planning tasks in simulated environments with multiple sub-goals. In this hierarchical structure, the network is capable of 1) learning one task with multiple sub-goals simultaneously; 2) extracting attentions of states according to changing sub-goals during the learning process; 3) reusing the well-trained network of sub-goals for other similar tasks with the same sub-goals. The states are defined as processed observations which are transmitted from the perception system of the autonomous vehicle. A hybrid reward mechanism is designed for different hierarchical layers in the proposed HRL structure. Compared to traditional RL methods, our algorithm is more sample-efficient since its modular design allows reusing the policies of sub-goals across similar tasks. The results show that the proposed method converges to an optimal policy faster than traditional RL methods.

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Zhiqian Qiao, Zachariah Tyree, Priyantha Mudalige, Jeff Schneider, John M. Dolan. 2019-11-09. Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning. https://arxiv.org/abs/1911.03799

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