mlsanityData to 14 Oct 2026

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

arXiv 2404.08523 ↗First posted 12 Apr 2024cs.LG cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 45 references that are in the map. First posted on arXiv 12 Apr 2024.

Builds on

8 of 45 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  4. 2015Trust Region Policy Optimizationcited by 2,580
  5. 2019Searching for MobileNetV3cited by 1,994
  6. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  7. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  8. 2023Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approachcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Spatiotemporal Wildfire Prediction and Reinforcement Learning for Helitack Suppression
  2. 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learning
  3. 2024Wildfire Autonomous Response and Prediction Using Cellular Automata (WARP-CA)
  4. 2026Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
  5. 2025JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning
  6. 2025A Probabilistic Approach to Wildfire Spread Prediction Using a Denoising Diffusion Surrogate Model
  7. 2026FIRE-VLM: A Vision-Language-Driven Reinforcement Learning Framework for UAV Wildfire Tracking in a Physics-Grounded Fire Digital Twin
  8. 2025BoreaRL: A Multi-Objective Reinforcement Learning Environment for Climate-Adaptive Boreal Forest Management