标题： 应用控制中的深度强化学习：挑战、分析与见解 显示英文标题

标题： Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights

摘要： 在过去十年中，采用深度神经网络来增强传统强化学习性能方面取得了显著进展。 一个重要的里程碑是深度Q网络（DQN）的开发，它在一系列Atari游戏中实现了人类水平的性能，展示了深度学习在稳定和扩展强化学习方面的潜力。 随后，对连续控制算法的扩展为控制领域开辟了一个新范式，这一范式比近年来文献中任何经典控制方法都更受关注。 这些发展也展示了在推进数据驱动、无模型的控制算法以及实现更高自主性方面的强大潜力。 然而，这些方法的应用仍然主要局限于模拟和游戏环境，目前仍在努力将其扩展到实际应用中。 在实现这种部署之前，有必要对其在应用控制问题上的性能有坚实而定量的理解。 本文对这些方法在四个不同的基准问题上进行了比较分析，并给出了实现结果。 这种分析提供了细致而系统的评估，以阐明深度强化学习方法在应用控制环境中的实际能力和局限性。 显示英文摘要

摘要： Over the past decade, remarkable progress has been made in adopting deep neural networks to enhance the performance of conventional reinforcement learning. A notable milestone was the development of Deep Q-Networks (DQN), which achieved human-level performance across a range of Atari games, demonstrating the potential of deep learning to stabilise and scale reinforcement learning. Subsequently, extensions to continuous control algorithms paved the way for a new paradigm in control, one that has attracted broader attention than any classical control approach in recent literature. These developments also demonstrated strong potential for advancing data-driven, model-free algorithms for control and for achieving higher levels of autonomy. However, the application of these methods has remained largely confined to simulated and gaming environments, with ongoing efforts to extend them to real-world applications. Before such deployment can be realised, a solid and quantitative understanding of their performance on applied control problems is necessary. This paper conducts a comparative analysis of these approaches on four diverse benchmark problems with implementation results. This analysis offers a scrutinising and systematic evaluation to shed light on the real-world capabilities and limitations of deep reinforcement learning methods in applied control settings.