  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
