mlsanityData to 7 Oct 2026

Robust Reinforcement Learning Under Minimax Regret for Green Security

arXiv 2106.08413 ↗First posted 15 Jun 2021cs.LG cs.AI cs.MAcited by 12

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Abstract (arXiv)

Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly, the deterrence effect of patrols on adversaries' future behavior makes patrol planning a sequential decision-making problem. Therefore, we focus on robust sequential patrol planning for green security following the minimax regret criterion, which has not been considered in the literature. We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy. MIRROR uses two reinforcement learning-based oracles and solves a restricted game considering limited defender strategies and parameter values. We evaluate MIRROR on real-world poaching data.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 13 of its 49 references that are in the map. First posted on arXiv 15 Jun 2021.

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13 of 49 references.

References · 13

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2017Robust Adversarial Reinforcement Learningcited by 439
  3. 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 265
  4. 2018Reinforcement Learning with Perturbed Rewardscited by 76
  5. 2019Risk Averse Robust Adversarial Reinforcement Learningcited by 54
  6. 2021Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomizationcited by 35
  7. 2018Deep Reinforcement Learning for Green Security Games with Real-Time Informationcited by 19
  8. 2020Learning to Play Sequential Games versus Unknown Opponentscited by 18
  9. 2020Dual-Mandate Patrols: Multi-Armed Bandits for Green Securitycited by 14
  10. 2019Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluationscited by 11
  11. 2020Minimax Regret Optimisation for Robust Planning in Uncertain Markov Decision Processescited by 10
  12. 2020Double Oracle Algorithm for Computing Equilibria in Continuous Gamescited by 7
1 more
  1. 2018Exploiting Data and Human Knowledge for Predicting Wildlife Poachingcited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 1; 2025: 4.

12 papers.

  1. 2024Policy Space Response Oracles: A Surveycited by 10
  2. 2022Learning to Mitigate AI Collusion on Economic Platformscited by 9
  3. 2025Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Datacited by 8
  4. 2021Restless and Uncertain: Robust Policies for Restless Bandits via Deep Multi-Agent Reinforcement Learningcited by 7
  5. 2025Robust Optimization with Diffusion Models for Green Securitycited by 4
  6. 2022Ranked Prioritization of Groups in Combinatorial Bandit Allocationcited by 3
  7. 2022A Simulation Environment and Reinforcement Learning Method for Waste Reductioncited by 3
  8. 2023Conservation Tools: The Next Generation of Engineering--Biology Collaborationscited by 2
  9. 2023Reflections from the Workshop on AI-Assisted Decision Making for Conservationcited by 2
  10. 2023Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regretcited by 2
  11. 2025Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservationcited by 0
  12. 2025PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defensecited by 0