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ACTOR-CRITIC REINFORCEMENT LEARNING FOR ENERGY OPTIMIZATION IN HYBRID PRODUCTION ENVIRONMENTS

Dorothea Schwung, Andreas Schwung, Steven X. Ding

Abstract


This paper presents a centralized approach for energy optimization in large scale industrial production systems based on an actor-critic reinforcement learning (ACRL) framework. The objective of the on-line capable self-learning algorithm is the optimization of the energy consumption of a production process while meeting certain manufacturing constraints like a demanded throughput. Our centralized ACRL algorithm works with two artificial neural networks (ANN) for function approximation using Gaussian radial-basis functions (RBF), one for the critic and another for the actor, respectively. This kind of actorcritic design enables the handling of both, a discrete and continuous state and action space, which is essential for hybrid systems where discrete and continuous actuator behavior is combined. The ACRL algorithm is exemplary validated on a dynamic simulation model of a bulk good system for the task of supplying bulk good to a subsequent dosing section while consuming as low energy as possible. The simulation results clearly show the applicability and capability of our machine learning (ML) approach for energy optimization in hybrid production environments.

Keywords


Machine learning; self-learning; actor-critic reinforcement learning; radial-basis function neural networks; manufacturing systems; hybrid systems; energy optimization.

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References


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