论文标题
强化学习申请的权力和问责制，
Power and accountability in reinforcement learning applications to environmental policy
论文作者
论文摘要
机器学习（ML）方法已经渗透到环境决策，从处理地球系统上的高维数据到监视遵守环境法规。在可解决紧迫环境问题的ML技术（例如，气候变化，生物多样性丧失），增强学习（RL）可能会持有最大的前景，并带来最紧迫的危险。本文探讨了由RL驱动的政策如何限制环境领域中的现有权力关系，同时还为确保公平和负责的环境决策过程带来了独特的挑战。我们利用从RL应用程序到缓解气候变化和渔业管理的示例来探讨RL技术如何在资源使用者，管理机构和私营企业之间转移功率分布。
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.
