Control of 1-D and 2-D coupled map lattices through reinforcement learning

Abstract

In this paper we show that coupled 1-D and 2-D logistic map lattices can be controlled to different types of behaviour through a recently introduced control algorithm (S. Gadaleta and G. Dangelmayr, Chaos 9, 775-88 (1999)) which is based on reinforcement learning. The control policy is established through information from the local neighborhood of a subsystem and does not require any explicit knowledge on system dynamics or location of desired patterns. The algorithm is applicable in noisy and nonstationary environments. We demonstrate that the control policies established from interaction with small systems can be applied to larger systems and combined to trigger the formation of new states.