A reinforcement learning based neural multiagent system for control of a combustion process

We present a control scheme based on reinforcement learning for an industrial hard-coal combustion process in a power plant. To comply with the great demands on environmental protection, the plant operator is interested in a minimization of the nitrogen oxides emission, while other process parameters have to be kept within predefined limits. To cope with both the tremendous action and situation space of the power plant, we present a multiagent reinforcement system consisting of 4 agents, which are realized by relatively simple neural function approximators. We demonstrate, that our multiagent system was able to significantly reduce the overall air consumption of the real combustion process of the power plant