JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning

• URL: http://arxiv.org/abs/2512.06102v1
• Date: Fri, 05 Dec 2025 19:06:07 GMT
• Title: JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning
• Authors: Ufuk Çakır, Victor-Alexandru Darvariu, Bruno Lacerda, Nick Hawes,
• Abstract summary: We introduce $textttJaxWildfire$, a simulator underpinned by a principled probabilistic fire spread model based on cellular automata.<n>We demonstrate that $textttJaxWildfire$ achieves 6-35x speedup over existing software and enables gradient-based optimization of simulator parameters.<n>Our work is an important step towards enabling the advancement of RL techniques for managing natural hazards.
• Score: 17.674265727888063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence methods are increasingly being explored for managing wildfires and other natural hazards. In particular, reinforcement learning (RL) is a promising path towards improving outcomes in such uncertain decision-making scenarios and moving beyond reactive strategies. However, training RL agents requires many environment interactions, and the speed of existing wildfire simulators is a severely limiting factor. We introduce $\texttt{JaxWildfire}$, a simulator underpinned by a principled probabilistic fire spread model based on cellular automata. It is implemented in JAX and enables vectorized simulations using $\texttt{vmap}$, allowing high throughput of simulations on GPUs. We demonstrate that $\texttt{JaxWildfire}$ achieves 6-35x speedup over existing software and enables gradient-based optimization of simulator parameters. Furthermore, we show that $\texttt{JaxWildfire}$ can be used to train RL agents to learn wildfire suppression policies. Our work is an important step towards enabling the advancement of RL techniques for managing natural hazards.

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