Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We leverage examples from RL applications to climate change mitigation and fisheries management to explore how RL technologies shift the distribution of power between resource users, governing bodies, and private industry.

翻译：机械学习方法已经渗透到环境决策中,从处理地球系统的高维数据到监测遵守环境规章的情况。在可用于解决紧迫环境问题(如气候变化、生物多样性丧失)的ML技术中,加强学习技术(RL)可能带来最大的希望,并带来最紧迫的危险。本文探讨了RL驱动的政策如何在环境领域重新划分现有的权力关系,同时也对确保公平和负责的环境决策过程提出了独特的挑战。我们利用从RL应用到减缓气候变化和渔业管理的例子,探索RL技术如何改变资源使用者、理事机构和私营工业之间的权力分配。