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- W4378619997 abstract "From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’." @default.
- W4378619997 created "2023-05-29" @default.
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- W4378619997 date "2023-05-29" @default.
- W4378619997 modified "2023-10-18" @default.
- W4378619997 title "Bridging adaptive management and reinforcement learning for more robust decisions" @default.
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- W4378619997 doi "https://doi.org/10.1098/rstb.2022.0195" @default.
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