1. 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 deep reinforcement learning (RL) to help tackle the most challenging conservation decision problems. We provide a conceptual and technical introduction to deep RL as well as annotated code so that researchers can adopt, evaluate and extend these approaches.2. RL explicitly focuses on designing an agent who interacts with an environment that is dynamic and uncertain. Deep RL is the subfield of RL that incorporates deep neural networks into the agent. We train deep RL agents to solve sequential decision-making problems in setting fisheries quotas and managing ecological tipping points.3. We show that a deep RL agent is able to learn a nearly optimal solution for the fisheries management problem. For the tipping point problem, we show that a deep RL agent can outperform a sensible rule-of-thumb strategy. 4. Our results demonstrate that deep RL has the potential to solve challenging decision problems in conservation. While this potential may be compelling, the challenges involved in successfully deploying RL-based management to realistic scenarios are formidable-the required expertise and computational cost may place these applications beyond the reach of all but large, international technology firms. Ecologists must establish a better understanding of how these algorithms work and fail if we are to realize this potential and avoid the pitfalls such a transition would bring. We ultimately set forth a research framework based on well-posed, public challenges so that ecologists and computer scientists can collaborate towards solving hard decision-making problems in conservation.

No abstract

In the current age of a rapidly changing environment, it is becoming increasingly important to study critical transitions and how to best anticipate them. Critical transitions pose extremely challenging forecasting problems, which necessitate informative uncertainty estimation rather than point forecasts. In this study, we apply some of the most cutting edge deep learning methods for probabilistic time series forecasting to several classic ecological models that examine critical transitions. Our analysis focuses on three different simulated examples of critical transitions: a Hopf bifurcation, a saddle‐node bifurcation and a stochastic transition. For each scenario, we compare the forecasts from four deep learning models, long‐short term memory networks, gated recurrent unit networks, lock recurrent neural networks and transformers, to forecasts from an ARIMA model and a MCMC estimated model that is given the true transition dynamics. We found that the deep learning models were able to perform comparably to the idealized MCMC model on the stochastic transition case, and generally in between the MCMC and ARIMA models on the Hopf and saddle‐node bifurcation examples. Our results establish that deep learning methods warrant further exploration on the challenging class of critical transition forecasting problems.

From out-competing grandmasters in chess to informing high-stakes healthcare decisions, emerging methods from artificial intelligence are increasingly capable of making complex and strategic decisions in diverse, high-dimensional and uncertain situations. But can these methods help us devise robust strategies for managing environmental systems under great uncertainty? Here we explore how reinforcement learning (RL), a subfield of artificial intelligence, approaches decision problems through a lens similar to adaptive environmental management: learning through experience to gradually improve decisions with updated knowledge. We review where RL holds promise for improving evidence-informed adaptive management decisions even when classical optimization methods are intractable and discuss technical and social issues that arise when applying RL to adaptive management problems in the environmental domain. Our synthesis suggests that environmental management and computer science can learn from one another about the practices, promises and perils of experience-based decision-making. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’.

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.