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arXiv · 2512.06102

JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning

Ufuk Çakır·Victor-Alexandru Darvariu·Bruno Lacerda·Nick Hawes
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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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Ufuk Çakır, Victor-Alexandru Darvariu, Bruno Lacerda, Nick Hawes. 2025-12-05. JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning. https://arxiv.org/abs/2512.06102

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