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.

该研究旨在针对绿色安全领域中的不确定性制定有韧性的顺序巡逻计划，以解决对保护者来说具有威慑效应的问题，并提出了一种基于强化学习的算法来找到一个更加稳健的决策策略。

绿色安全中极小后悔的强化学习

Robust Reinforcement Learning Under Minimax Regret for Green Security

We describe the semantic foundations for elicitation of generalized
additively independent (GAI) utilities using the minimax regret criterion, and
propose several new query types and strategies for this purpose. Computational
feasibility is obtained by exploiting the local GAI structure in the model. Our
results provide a practical approach for implementing preference-based
constrained configuration optimization as well as effective search in
multiattribute product databases.

在该研究中，我们使用最小化最大后悔标准描述了提取广义加性独立效用的语义基础，并提出了若干新的查询类型和策略；通过利用模型中的局部 GAI 结构，实现了计算可行性；所得结果提供了实现基于偏好的受限配置优化以及在多属性产品数据库中进行有效搜索的实用方法。