Fighting Fire with Algorithms - Lockheed's Reinforcement Learning-Based Wildfire Solution

Explore a 13-minute conference talk from Ray Summit 2024 showcasing Lockheed Martin's innovative deep reinforcement learning system for wildland firefighting. Learn how the team developed a sophisticated decision-aid system using rllib's hierarchical and multi-agent abstractions to generate optimal fire suppression strategies. Discover the two-level hierarchical agent structure that mirrors real-world wildfire incident command, implemented through RLlib's multi-agent capabilities and scaled with Ray Core and Tune. Examine the impressive results achieved in synthetically generated wildfire scenarios, where AI agents successfully contained 80% of incidents across varying wind and fuel conditions. Gain insights into the wildfire management simulator's development, training methodology using synthetic data, and future enhancements to the policy architecture. Understand how the team's collection of historical wildfire incident data from across the continental United States paves the way for real-world implementation of this AI-driven firefighting solution.