Negotiation agent based on Deep reinforcement learning for multi-agent cooperative distributed predictive control

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In this work, deep neural network trained by the model-free reinforcement learning method is proposed as a negotiation agent in multi-agent distributive model predictive control. The negotiation agent is implemented in the upper layer of a hierarchical control architecture based on distributed model predictive controller with pairwise negotiation fuzzy logic. The proposed is data-driven in order to achieve the minimization of any dynamic behavior index and the specification of constraints. Specifically, the deep neural network provides consensus coefficients to address the final control action applied by each agent. The proposed is successfully applied to a hydraulic system composed of eight interconnected tanks that are very difficult to control due to non-linearities nature and high interaction between its subsystems.