Training model updating method and system, intelligent agent, server and storage medium

Abstract

The invention discloses a training model updating method and system, an intelligent agent, a server and a storage medium. The method relates to the field of financial science and technology, and comprises the following steps: a federated learning server receives a first training model sent by each reinforcement learning agent running in a real-time environment, and receives a second training modelsent by a data server and obtained by training according to historical sample data; performing federated learning on the first training model and the second training model to obtain a federated model; and sending the federated model to the data server and each reinforcement learning agent, so that the data server and each reinforcement learning agent correspondingly update a training model according to the federated model. According to the method, the accuracy of obtaining the training result through the training model is improved, that is, the sample knowledge extraction capability of the training model is improved, and the stability of the training model is improved.

Description

technical field [0001] The present invention relates to the technical field of data processing of financial science and technology (Fintech), in particular to a training model updating method, system, agent, server and storage medium. Background technique [0002] With the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming into financial technology (Fintech), and data processing technology is no exception. However, due to the security and real-time requirements of the financial industry, It also places higher demands on technology. [0003] Nowadays, the training method of reinforcement learning applied in the industry is usually that the trained reinforcement learning agent (Agent) will maintain a fixed-scale experience pool (experience playback), and when the scale of the experience pool exceeds a certain size, the reinforcement learning agent will experience Forge...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to provide an update method, system, agent, server and storage medium for a training model, aiming to solve the problem that the existing model obtained through reinforcement learning training has a low ability to extract knowledge about samples, and the training obtained Technical issues with poor model stability

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