Design of semi-decentralized control laws for distributed-air-jet micromanipulators by reinforcement learning

Recently, a great deal of interest has been developed in learning in multi-agent systems to achieve decentralized control. Machine learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. In this paper, we propose a semi-decentralized reinforcement learning control approach in order to position and convey an object on a contact-free MEMS-based distributed-manipulation system. The experimental results validate the semi-decentralized reinforcement learning method as a way to design control laws for such distributed systems.