Feature extraction method and device based on reinforcement learning and computer device

Abstract

The invention relates to a feature extraction method and device based on reinforcement learning, and a computer device. The method comprises the steps of obtaining a feature extraction code of a learning object; wherein the feature extraction code is determined according to manual writing; acquiring state features of the learning object according to the feature extraction code; training a deep network structure based on reinforcement learning by adopting the state features; obtaining an optimal network structure and an optimal weight parameter of the trained deep network structure; generatingan optimal feature extraction strategy according to the optimal network structure and the optimal weight parameter; wherein the optimal feature extraction strategy is used for extracting portrait features of the insurance service user so as to analyze insurance demands of the insurance service user according to the portrait features. By adopting the method, the feature extraction codes are set tobe applied to model training, so that the feature extraction efficiency can be improved, namely, a modeling effect is used as a learning reward to stimulate a computer to continuously optimize a learning strategy so as to learn a new feature extraction mode.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a feature extraction method, device, computer-readable storage medium and computer equipment based on reinforcement learning. Background technique [0002] Reinforcement learning, also known as trial-and-error learning, is a machine learning algorithm that allows the agent to interact continuously in the environment of the learning object and learn according to the feedback incentive (reward) of the environment. The learning algorithm is not based on Any prior knowledge can be learned completely autonomously. According to different learning objects, there can be different agents. For example, when the learning object is insurance business, the agent can be an insured user in the insurance business, etc. [0003] When traditional reinforcement learning such as Deep Q Network (DQN) trains its own neural network model, it completely uses the data obtained by the machine...

AI Technical Summary

Problems solved by technology

[0004] Based on this, it is necessary to provide a feature extraction method, device, computer-readable storage medium and computer equipment based on reinforcement learning for the technical problem of low efficiency of feature extraction used in model training in the prior art

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