  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.
