1 research outputs found
Multi-Task Generative Adversarial Nets with Shared Memory for Cross-Domain Coordination Control
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