Reinforcement Learning for Wildfire Mitigation in Simulated Disaster
  Environments
Alexander Tapley Marissa Dotter Michael Doyle Aidan Fennelly Dhanuj Gandikota Savanna Smith Michael Threet Tim Welsh
Climate change has resulted in a year over year increase in adverse weather
and weather conditions which contribute to increasingly severe fire seasons.
Without effective mitigation, these fires pose a threat to life, property,
ecology, cultural heritage, and critical infrastructure. To better prepare for
and react to the increasing threat of wildfires, more accurate fire modelers
and mitigation responses are necessary. In this paper, we introduce SimFire, a
versatile wildland fire projection simulator designed to generate realistic
wildfire scenarios, and SimHarness, a modular agent-based machine learning
wrapper capable of automatically generating land management strategies within
SimFire to reduce the overall damage to the area. Together, this publicly
available system allows researchers and practitioners the ability to emulate
and assess the effectiveness of firefighter interventions and formulate
strategic plans that prioritize value preservation and resource allocation
optimization. The repositories are available for download at
https://github.com/mitrefireline.