Intelligent parallel training method based on simulation experiments

Abstract

Intelligent training is the process of using machine learning algorithms to train and optimize neural network agent models. The agent model achieves intelligent improvement through continuous trial and error training. Large scale training data is a necessary condition for intelligent learning training, which is usually difficult to obtain directly from the real world. How to generate a large amount of effective training data through simulation is an important research direction for intelligent agent training. This article proposes an intelligent parallel training method based on simulation experiments. By utilizing simulation experiment management, batch simulation experiment scenarios can be quickly generated, and nodes can be automatically deployed and run. Intelligent parallel training can be achieved through reasonable training architecture design and effective training process design. The simulation experiment management process of intelligent training is demonstrated through practical cases, and combined with training results, it is proven that the method proposed in this article improves the efficiency of intelligent training and the generalization of intelligent agents.

Keywords

• reinforcement learning|intelligent training|parallel training|simulation experiments