A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles

• URL: http://arxiv.org/abs/2008.01302v2
• Date: Sun, 8 Oct 2023 07:32:57 GMT
• Title: A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
• Authors: Teng Liu, Yuyou Yang, Wenxuan Xiao, Xiaolin Tang, Mingzhu Yin
• Abstract summary: Deep reinforcement learning (DRL) has emerged as a potent methodology for addressing artificial intelligence challenges. This article compares several DRL approaches for decision-making challenges encountered by autono-mous vehicles on freeways. A series of simulation experiments are conducted to assess the control performance of these DRL-enabled decision-making strategies.
• Score: 2.394554182452767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) has emerged as a pervasive and potent methodology for addressing artificial intelligence challenges. Due to its substantial potential for autonomous self-learning and self-improvement, DRL finds broad applications across various research domains. This article undertakes a comprehensive comparison of several DRL approaches con-cerning the decision-making challenges encountered by autono-mous vehicles on freeways. These techniques encompass common deep Q-learning (DQL), double deep Q-learning (DDQL), dueling deep Q-learning, and prioritized replay deep Q-learning. Initially, the reinforcement learning (RL) framework is introduced, fol-lowed by a mathematical establishment of the implementations of the aforementioned DRL methods. Subsequently, a freeway driving scenario for automated vehicles is constructed, wherein the decision-making problem is reformulated as a control opti-mization challenge. Finally, a series of simulation experiments are conducted to assess the control performance of these DRL-enabled decision-making strategies. This culminates in a comparative analysis, which seeks to elucidate the connection between autonomous driving outcomes and the learning char-acteristics inherent to these DRL techniques.

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