Over the past decades, the increase in both frequency and intensity of large-scale wildfires due to climate change has emerged as a significant natural threat. The pressing need to design resilient landscapes capable of withstanding such disasters has become paramount, requiring the development of advanced decision-support tools. Existing methodologies, including Mixed Integer Programming, Stochastic Optimization, and Network Theory, have proven effective but are hindered by computational demands, limiting their applicability. In response to this challenge, we propose using artificial intelligence techniques, specifically Deep Reinforcement Learning, to address the complex problem of firebreak placement in the landscape. We employ value-function based approaches like Deep Q-Learning, Double Deep Q-Learning, and Dueling Double Deep Q-Learning. Utilizing the Cell2Fire fire spread simulator combined with Convolutional Neural Networks, we have successfully implemented a computational agent capable of learning firebreak locations within a forest environment, achieving good results. Furthermore, we incorporate a pre-training loop, initially teaching our agent to mimic a heuristic-based algorithm and observe that it consistently exceeds the performance of these solutions. Our findings underscore the immense potential of Deep Reinforcement Learning for operational research challenges, especially in fire prevention. Our approach demonstrates convergence with highly favorable results in problem instances as large as 40 x 40 cells, marking a significant milestone in applying Reinforcement Learning to this critical issue. To the best of our knowledge, this study represents a pioneering effort in using Reinforcement Learning to address the aforementioned problem, offering promising perspectives in fire prevention and landscape management

翻译：过去几十年来，气候变化导致大规模野火的频率和强度显著增加，已成为一项重大自然威胁。构建能够抵御此类灾害的适应性景观的迫切需求日益凸显，这要求开发先进的决策支持工具。现有方法（包括混合整数规划、随机优化和网络理论）虽已被证明有效，但受限于计算需求，限制了其适用性。为应对这一挑战，我们提出利用人工智能技术，特别是深度强化学习，来解决景观中防火带布局这一复杂问题。我们采用了基于价值函数的方法，如深度Q学习、双重深度Q学习和竞争双重深度Q学习。通过结合Cell2Fire火灾蔓延模拟器与卷积神经网络，我们成功实现了一个能够在森林环境中学习防火带位置的计算智能体，并取得了良好效果。此外，我们引入预训练循环，先让智能体模仿基于启发式的算法，并观察到其表现始终优于这些解法的性能。我们的研究结果凸显了深度强化学习在运筹学挑战（尤其是火灾预防）中的巨大潜力。该方法在高达40×40单元的问题实例中展现出收敛性，取得了极为理想的结果，标志着强化学习应用于这一关键问题的重要里程碑。据我们所知，本研究率先将强化学习用于解决上述问题，为火灾预防与景观管理提供了富有前景的新视角。