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From out-competing grandmasters in chess to informing high-stakes healthcaredecisions, emerging methods from artificial intelligence are increasinglycapable of making complex and strategic decisions in diverse, high-dimensional,and uncertain situations. But can these methods help us devise robuststrategies for managing environmental systems under great uncertainty? Here weexplore how reinforcement learning, a subfield of artificial intelligence,approaches decision problems through a lens similar to adaptive environmentalmanagement: learning through experience to gradually improve decisions withupdated knowledge. We review where reinforcement learning (RL) holds promisefor improving evidence-informed adaptive management decisions even whenclassical optimization methods are intractable. For example, model-free deep RLmight help identify quantitative decision strategies even when models arenonidentifiable. Finally, we discuss technical and social issues that arisewhen applying reinforcement learning to adaptive management problems in theenvironmental domain. Our synthesis suggests that environmental management andcomputer science can learn from one another about the practices, promises, andperils of experience-based decision-making.

Bridging adaptive management and reinforcement learning for more robust decisions