Reinforcement learning reward self-learning method in discrete manufacturing scene

Abstract

The invention discloses a reinforcement learning reward self-learning method in a discrete manufacturing scene. The method comprises the following steps: 1, refining the process of the current production line, wherein g belongs to G = {g1, g2,..., gN}, and the intelligent agent reaches a preset target g and is recorded as an interaction sequence episode; according to the initial parameters, obtaining multiple sections of episodes corresponding to the g1 as a target, taking state actions in the episodes and a state difference value delta as a training data set to be input into a GPR module, andobtaining a system state transition model based on state difference; enabling the intelligent Agent to continue to interact with the environment to obtain a new state st, and enabling a Reward network to output r (st), enabling an Actor network to output a (st), enabling a Critic network to output V (st) and enabling a GPR module to output value function Vg as the updating direction of the whole;when the absolute value of Vg-V (st) is smaller than epsilon, considering that award function learning under the current procedure is completed, and carrying out parameter storage of the Reward network; continuously carrying out interaction, and generating the following sub-target g < n + 1 > as the episodes of the updating direction for updating the GPR; and when the set target G = {g1, g2,...,gN} is all realized in sequence, finishing the process learning of the production line.

Description

technical field [0001] The present invention relates to the technical field of deep reinforcement learning, and more specifically, relates to a reinforcement learning reward learning method in discrete manufacturing scenarios. Background technique [0002] The manufacturing industry can be generally divided into process manufacturing and discrete manufacturing according to the characteristics of its product manufacturing process. Compared with process manufacturing, discretely manufactured products are often processed and assembled from multiple parts through a series of discontinuous processes, mainly including machining and assembly industries such as machining and machine tools. [0003] For the processing and production process of discrete manufacturing enterprises. The whole production process is often decomposed into many processing tasks, and each processing task does not require much processing resources, but parts are often processed with different types and requir...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that in the existing discrete manufacturing production line, the adjustment of the control parameters of each process of product manufacturing mainly depends on the work experience of the staff, it provides a self-learning method for reinforcement learning and rewards in the discrete manufacturing scene. Using the method of deep reinforcement learning to realize the learning of control parameters of production line equipment

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