mlsanityData to 7 Oct 2026

Deep Reinforcement Learning for Conservation Decisions

arXiv 2106.08272 ↗First posted 15 Jun 2021cs.LG q-bio.QMcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 88 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

20 of 88 references.

References · 20

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2015Continuous control with deep reinforcement learningcited by 3,279
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8 more
  1. 2017Deep Reinforcement Learning for De-Novo Drug Designcited by 161
  2. 2017Imagination-Augmented Agents for Deep Reinforcement Learningcited by 137
  3. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 103
  4. 2018Temporal Difference Models: Model-Free Deep RL for Model-Based Controlcited by 89
  5. 2016Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Criticcited by 77
  6. 2017A multi-agent reinforcement learning model of common-pool resource appropriationcited by 68
  7. 2020Measuring the Algorithmic Efficiency of Neural Networkscited by 51
  8. 2016Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPUcited by 46

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.

5 papers.

  1. 2023Reflections from the Workshop on AI-Assisted Decision Making for Conservationcited by 2
  2. 2022Power and accountability in reinforcement learning applications to environmental policycited by 1
  3. 2023Pretty darn good control: when are approximate solutions better than approximate modelscited by 1
  4. 2024Using machine learning to inform harvest control rule design in complex fishery settingscited by 0
  5. 2025BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Managementcited by 0