  Can machine learning help us make better decisions about a changing planet?
In this paper, we illustrate and discuss the potential of a promising corner of
machine learning known as _reinforcement learning_ (RL) to help tackle the most
challenging conservation decision problems. RL is uniquely well suited to
conservation and global change challenges for three reasons: (1) RL explicitly
focuses on designing an agent who _interacts_ with an environment which is
dynamic and uncertain, (2) RL approaches do not require massive amounts of
data, (3) RL approaches would utilize rather than replace existing models,
simulations, and the knowledge they contain. We provide a conceptual and
technical introduction to RL and its relevance to ecological and conservation
challenges, including examples of a problem in setting fisheries quotas and in
managing ecological tipping points. Four appendices with annotated code provide
a tangible introduction to researchers looking to adopt, evaluate, or extend
these approaches.
