  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.
