Research on Intelligent Optimization Algorithm of Arranging Based on Reinforcement Learning

Reinforcement learning is an important machine learning method, which has been widely used in the fields of intelligent robot, economics, industrial manufacturing, game and so on. Reinforcement learning is a kind of learning from environmental state to action mapping, and it is expected that action will get the maximum cumulative reward from the environment. Reinforcement learning can be divided into two basic processes: learning process and planning process. Learning refers to the process of directly interacting with the environment, and in this process, using the obtained direct experience to update the value function to improve the strategy. Planning refers to the process of learning in the environmental model, and in this process, the simulation experience generated by the model is used to update the value function to improve the strategy. The research of intelligent algorithm is not only conducive to all aspects of the national economy, but also brings convenience to people's activities. It has certain theoretical significance and great application prospects. This paper summarizes the research trends of optimization algorithms, reviews the development status of swarm intelligence algorithms, and provides a theoretical basis for the follow-up research of swarm intelligence algorithms. This paper summarizes and reviews the relevant theoretical methods and Application Research of using deep reinforcement learning method to solve combinatorial optimization problems in recent years, summarizes and summarizes its basic principles, relevant methods and application research, and points out some problems to be solved in this direction in the future.