基于深度强化学习的自动驾驶车辆高速公路决策对比分析
A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
AI总结：
本文针对自动驾驶车辆高速公路决策问题，对比了DQL、DDQL等四种深度强化学习方法，通过仿真实验分析了不同方法的控制性能及其学习特性与驾驶效果的关联。
摘要：
深度强化学习（DRL）已成为解决人工智能问题的一种普遍且有效的方法。凭借其自主学习与自我提升的巨大潜力，DRL在各类研究领域中得到了广泛应用。本文针对自动驾驶车辆在高速公路上遇到的决策难题，对多种DRL方法开展了全面对比。这些技术包括常规深度Q学习（DQL）、双重深度Q学习（DDQL）、对决深度Q学习以及优先回放深度Q学习。文章首先介绍了强化学习（RL）框架，随后对上述DRL方法的实现进行了数学推导。接着，构建了自动驾驶车辆的高速公路驾驶场景，并将其中的决策问题重新表述为控制优化问题。最后，开展了一系列仿真实验以评估这些基于DRL的决策策略的控制性能，并最终进行对比分析，旨在阐明自动驾驶效果与这些DRL技术固有学习特性之间的关联。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
