  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, 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 reinforcement learning (RL) holds promise
for improving evidence-informed adaptive management decisions even when
classical optimization methods are intractable. For example, model-free deep RL
might help identify quantitative decision strategies even when models are
nonidentifiable. Finally, we discuss technical and social issues that arise
when applying reinforcement learning 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.
