JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning

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 textttJaxWildfire, a simulator underpinned by a principled probabilistic fire spread model based on cellular automata. It is implemented in JAX and enables vectorized simulations using textttvmap, allowing high throughput of simulations on GPUs. We demonstrate that textttJaxWildfire achieves 6-35x speedup over existing software and enables gradient-based optimization of simulator parameters. Furthermore, we show that textttJaxWildfire 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.