Highway Lane Change Decision-Making via Attention-Based Deep Reinforcement Learning
نویسندگان
چکیده
Deep reinforcement learning (DRL), combining the perception capability of deep (DL) and decision-making (RL) [1], has been widely investigated for autonomous driving tasks. In this letter, we would like to discuss impact different types state input on performance DRL-based lane change decision-making.
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ژورنال
عنوان ژورنال: IEEE/CAA Journal of Automatica Sinica
سال: 2022
ISSN: ['2329-9274', '2329-9266']
DOI: https://doi.org/10.1109/jas.2021.1004395