Investigations on the Design and Implementation of Reinforcement Learning Using Deep Learning Neural Networks

This paper investigates the design and MATLAB/Simulink implementation of two intelligent neural reinforcement learning control algorithms based on deep learning neural network structures (RL DLNNs), in the case of a complex Heating Ventilation Air Conditioning (HVAC) centrifugal chiller system (CCS) selected as a case study. This system poses significant control-related challenges due to its high dimensionality and strong nonlinearity multi-input multi-output (MIMO) structure, coupled with strong constraints and a substantial impact of measured disturbance on tracking performance. As a beneficial vehicle for "proof of concept", two simplified CCS MIMO models were derived. At the same time, many simulations were run to demonstrate the effectiveness of both RL DLNN control algorithm implementations compared to two conventional control algorithms. Moreover, the experiments involving the two investigated data-driven advanced neural control algorithms prove their high potential to adapt to various types of nonlinearities, singularities, dimensions, disruptions, constraints, and uncertainties that inherently characterize real-world processes.