Advancing Forest Fire Prevention: Deep Reinforcement Learning for Effective Firebreak Placement

TL;DR

This paper introduces a novel application of Deep Reinforcement Learning techniques to optimize firebreak placement for wildfire prevention, demonstrating effective learning and superior performance over heuristic methods in large-scale forest environments.

Contribution

It pioneers the use of Deep Reinforcement Learning for firebreak placement, integrating CNNs and simulation to improve wildfire prevention strategies.

Findings

Deep RL agents successfully learned firebreak placement in 40x40 environments.

Pre-training with heuristics enhances RL agent performance.

The approach converges reliably in large-scale problem instances.

Abstract

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