2024
DOI: 10.1007/s10462-024-10706-5
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Reinforcement learning applications in environmental sustainability: a review

Maddalena Zuccotto,
Alberto Castellini,
Davide La Torre
et al.

Abstract: Environmental sustainability is a worldwide key challenge attracting increasing attention due to climate change, pollution, and biodiversity decline. Reinforcement learning, initially employed in gaming contexts, has been recently applied to real-world domains, including the environmental sustainability realm, where uncertainty challenges strategy learning and adaptation. In this work, we survey the literature to identify the main applications of reinforcement learning in environmental sustainability and the p… Show more

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Cited by 4 publications
(2 citation statements)
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“…24 Several review papers indicated that the links of DRL study to minimize carbon emission 15 and balance the conflicting objectives of WWTP performances are imperative and could be promising future directions. 25 Although researchers have explored WWTP operation optimization with multiple objectives, such as effluent quality and energy consumption, using reinforcement learning 26 or other machine learning tools, 27 they neither ignored the environmental effect of GHG directly emitted from the process nor dug into the gambling relations or synergy effects of multiple objectives. 28 Chen et al considered GHG emissions in their multiobjective optimal control study of WWTPs based on life cycle assessment and referenced emission factors from related works.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…24 Several review papers indicated that the links of DRL study to minimize carbon emission 15 and balance the conflicting objectives of WWTP performances are imperative and could be promising future directions. 25 Although researchers have explored WWTP operation optimization with multiple objectives, such as effluent quality and energy consumption, using reinforcement learning 26 or other machine learning tools, 27 they neither ignored the environmental effect of GHG directly emitted from the process nor dug into the gambling relations or synergy effects of multiple objectives. 28 Chen et al considered GHG emissions in their multiobjective optimal control study of WWTPs based on life cycle assessment and referenced emission factors from related works.…”
Section: Introductionmentioning
confidence: 99%
“…Croll et al have evaluated four common DRL algorithms systematically to minimize treatment energy in wastewater treatment control . Several review papers indicated that the links of DRL study to minimize carbon emission and balance the conflicting objectives of WWTP performances are imperative and could be promising future directions …”
Section: Introductionmentioning
confidence: 99%