Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control

TL;DR

This paper introduces a multi-task generative adversarial network with shared memory designed for cross-domain coordination control, enabling direct generation of decision policies from raw data and improving performance across tasks.

Contribution

It proposes a novel multi-task GAN with shared memory that enhances cross-task knowledge transfer and decision policy generation in discrete-time nonlinear systems.

Findings

Improves task performance by leveraging related tasks.

Successfully applied to a smart factory manufacturing testbed.

Demonstrates effectiveness on multiple nonlinear control tasks.

Abstract

Generating sequential decision process from huge amounts of measured process data is a future research direction for collaborative factory automation, making full use of those online or offline process data to directly design flexible make decisions policy, and evaluate performance. The key challenges for the sequential decision process is to online generate sequential decision-making policy directly, and transferring knowledge across tasks domain. Most multi-task policy generating algorithms often suffer from insufficient generating cross-task sharing structure at discrete-time nonlinear systems with applications. This paper proposes the multi-task generative adversarial nets with shared memory for cross-domain coordination control, which can generate sequential decision policy directly from raw sensory input of all of tasks, and online evaluate performance of system actions in discrete-time nonlinear systems. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results on three groups of discrete-time nonlinear control tasks show that our proposed model can availably improve the performance of task with the help of other related tasks.