Deep Reinforcement Learning (DRL) is being used in many domains. One of the biggest advantages of DRL is that it enables the continuous improvement of a learning agent. Secondly, the DRL framework is robust and flexible enough to be applicable to problems of varying nature and domain. Presented work is evidence of using the DRL technique to solve an Optimal Power Flow (OPF) problem. Two classical algorithms have been presented to solve the OPF problem. The drawbacks of the vanilla DRL application are discussed, and an algorithm is suggested to improve the performance. Secondly, a reward function for the OPF problem is presented that enables the solution of inherent issues in DRL. Reasons for divergence and degeneration in DRL are discussed, and the correct strategy to deal with them with respect to OPF is presented.

翻译：深度强化学习(DRL)正在许多领域使用。 DRL的最大优势之一是它能够不断改进学习机构。 其次, DRL框架是强大和灵活的,足以适用于不同性质和领域的问题。 介绍的工作是使用DRL技术解决最佳电力流动(OPF)问题的证据。 提出了两种古典算法来解决OPF问题。 讨论了香草DRL应用的缺点,并建议了一种算法来改进业绩。 其次,提出了对OPF问题的奖励功能,使DRL的内在问题得以解决。 讨论了DRL差异和退化的原因,并提出了处理OPF问题的正确战略。