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Machine learning (ML) methods already permeate environmental decision-making,from processing high-dimensional data on earth systems to monitoring compliancewith environmental regulations. Of the ML techniques available to addresspressing environmental problems (e.g., climate change, biodiversity loss),Reinforcement Learning (RL) may both hold the greatest promise and present themost pressing perils. This paper explores how RL-driven policy refractsexisting power relations in the environmental domain while also creating uniquechallenges to ensuring equitable and accountable environmental decisionprocesses. We leverage examples from RL applications to climate changemitigation and fisheries management to explore how RL technologies shift thedistribution of power between resource users, governing bodies, and privateindustry.

Power and accountability in reinforcement learning applications to environmental policy