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.

翻译：从在国际象棋中击败特级大师，到为高风险医疗决策提供依据，人工智能领域涌现的新方法正日益能够在多样化、高维且充满不确定性的情境中做出复杂而具战略性的决策。但这些方法能否帮助我们在巨大不确定性下为环境系统管理制定稳健策略？本文探讨了人工智能子领域——强化学习如何以类似于自适应环境管理的视角处理决策问题：通过经验学习，逐步改进决策并更新知识。我们回顾了强化学习在经典优化方法难以处理的情况下，如何有望改进基于证据的自适应管理决策。例如，即使在模型不可辨识的情况下，无模型深度强化学习仍可能帮助识别量化决策策略。最后，我们讨论了将强化学习应用于环境领域自适应管理问题时出现的技术与社会议题。我们的综合研究表明，环境管理与计算机科学可以相互学习基于经验决策的实践、前景与风险。