Multi-objective Reinforcement Learning for Energy-Efficient Industrial Control

Georg Schäfer; R. L. Seliger; Jakob Rehrl; Stefan Huber; Simon Hirlaender

doi:10.1007/978-3-032-02003-1_6

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Multi-objective Reinforcement Learning for Energy-Efficient Industrial Control

Georg Schäfer R. L. Seliger Jakob Rehrl Stefan Huber Simon Hirlaender

Year: 2025 Communications in computer and information science Pages: 67-72 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-032-02003-1_6

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Keywords:

Reinforcement learning Control (management) Computer science Reinforcement Energy (signal processing) Artificial intelligence Materials science Physics Composite material

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Extremum Seeking Control Systems

Physical Sciences → Engineering → Control and Systems Engineering

Smart Grid Security and Resilience

Physical Sciences → Engineering → Control and Systems Engineering

Advanced Control Systems Optimization

Physical Sciences → Engineering → Control and Systems Engineering

Multi-objective Reinforcement Learning for Energy-Efficient Industrial Control

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