Deep reinforcement learning (DRL) has recently been adopted in a wide range of physics and engineering domains for its ability to solve decision-making problems that were previously out of reach due to a combination of non-linearity and high dimensionality. In the last few years, it has spread in the field of computational mechanics, and particularly in fluid dynamics, with recent applications in flow control and shape optimization. In this work, we conduct a detailed review of existing DRL applications to fluid mechanics problems. In addition, we present recent results that further illustrate the potential of DRL in Fluid Mechanics. The coupling methods used in each case are covered, detailing their advantages and limitations. Our review also focuses on the comparison with classical methods for optimal control and optimization. Finally, several test cases are described that illustrate recent progress made in this field. The goal of this publication is to provide an understanding of DRL capabilities along with state-of-the-art applications in fluid dynamics to researchers wishing to address new problems with these methods.

深度强化学习（DRL）最近已经在物理和工程领域广泛采用，因为它能够解决由于非线性和高维相结合而无法解决的决策问题。在过去的几年中，它已经在计算力学领域，尤其是在流体动力学领域得到了扩展，最近在流量控制和形状优化中得到了应用。在这项工作中，我们对现有的DRL应用程序解决流体力学问题进行了详细的审查。此外，我们提供了最近的结果，这些结果进一步说明了DRL在流体力学中的潜力。涵盖了每种情况下使用的耦合方法，详细说明了它们的优点和局限性。我们的审查还着重于与经典方法的比较，以实现最佳控制和优化。最后，描述了几个测试案例，这些案例说明了该领域的最新进展。该出版物的目的是为希望解决这些方法新问题的研究人员提供DRL功能以及流体动力学的最新应用的理解。