This paper discusses the challenges of applying reinforcement techniques to real-world environmental monitoring problems and proposes innovative solutions to overcome them. In particular, we focus on safety, a fundamental problem in RL that arises when it is applied to domains involving humans or hazardous uncertain situations. We propose to use deep neural networks, formal verification, and online refinement of domain knowledge to improve the transparency and efficiency of the learning process, as well as the quality of the final policies. We present two case studies, specifically (i) autonomous water monitoring and (ii) smart control of air quality indoors. In particular, we discuss the challenges and solutions to these problems, addressing crucial issues such as anomaly detection and prevention, real-time control, and online learning. We believe that the proposed techniques can be used to overcome some limitations of RL, providing safe and efficient solutions to complex and urgent problems.

Safe and Efficient Reinforcement Learning for Environmental Monitoring

Federico Bianchi;Davide Corsi;Luca Marzari;Daniele Meli;Francesco Trotti;Maddalena Zuccotto;Alberto Castellini;Alessandro Farinelli
2023-01-01

Abstract

This paper discusses the challenges of applying reinforcement techniques to real-world environmental monitoring problems and proposes innovative solutions to overcome them. In particular, we focus on safety, a fundamental problem in RL that arises when it is applied to domains involving humans or hazardous uncertain situations. We propose to use deep neural networks, formal verification, and online refinement of domain knowledge to improve the transparency and efficiency of the learning process, as well as the quality of the final policies. We present two case studies, specifically (i) autonomous water monitoring and (ii) smart control of air quality indoors. In particular, we discuss the challenges and solutions to these problems, addressing crucial issues such as anomaly detection and prevention, real-time control, and online learning. We believe that the proposed techniques can be used to overcome some limitations of RL, providing safe and efficient solutions to complex and urgent problems.
2023
Reinforcement Learning, Safety, Water Monitoring, Air Quality Management, Environmental Sustainability
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1113666
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