Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment

• URL: http://arxiv.org/abs/2007.09101v1
• Date: Thu, 16 Jul 2020 14:37:50 GMT
• Title: Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment
• Authors: Teng Liu, Xiaolin Tang, Jinwei Zhang, Wenbo Li, Zejian Deng, Yalian Yang
• Abstract summary: This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. Two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies. The control performance of the derived DM policies in safety and efficiency is analyzed.
• Score: 9.61519028373031
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.

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