Deep Reinforcement Learning for Conservation Decisions
This work targets conservation and global change challenges, offering a novel application of RL but is incremental in adapting existing methods to this domain.
The paper explores how reinforcement learning (RL) can address conservation decision problems, such as setting fisheries quotas and managing ecological tipping points, by leveraging its ability to interact with dynamic environments without requiring massive data.
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.