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

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.