Report
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning
| العنوان: | Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning |
|---|---|
| المؤلفون: | Rezaee, Kasra, Yadmellat, Peyman, Nosrati, Masoud S., Abolfathi, Elmira Amirloo, Elmahgiubi, Mohammed, Luo, Jun |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | Competent multi-lane cruising requires using lane changes and within-lane maneuvers to achieve good speed and maintain safety. This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction. While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action space according to high level behavioral decisions. We argue that this design allows principled modular extension of motion planning, in contrast to using either monolithic behavior cloning or a large set of hand-written rules. Moreover, we demonstrate that our state-action space abstraction allows transferring of the trained models without retraining from a simulated environment with virtually no dynamics to one with significantly more realistic dynamics. Together, these results suggest that our proposed hierarchical architecture is a promising way to allow reinforcement learning to be applied to complex multi-lane cruising in the real world. Comment: Paper presented at the IEEE Intelligent Transportation Systems Conference (ITSC) 2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2110.00650 |
| رقم الانضمام: | edsarx.2110.00650 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |