Research and Application Analysis of Reinforcement Learning Algorithms

With the continuous development of the times, there have been many significant breakthroughs in the field of reinforcement learning, not only reflected in the improvement of operational efficiency, but also in the emergence of new methods, all of which contribute to the continuous concretization of reinforcement learning in reality. This article mainly introduces the current research status of reinforcement learning, the introduction of common basic reinforcement learning algorithms such as value function estimation, policy gradient method, and Actor Critic algorithm. In recent years, a review of transfer learning, width planning and active learning based methods Olive, Reptile algorithm, evolutionary strategy (ES), two self play training schemes (Chainer and Pool), and the application analysis of reinforcement learning in the fields of gaming, robot control, transportation, healthcare, finance, and energy is conducted. This article aims to provide a review of the current research status in recent years, in order to provide a reference for the future development of reinforcement learning and indicate the existing problems in current research.