Uploaded October 2024 | Updated September 2026, 2 weeks ago
Lockheed Martin unveils its cutting-edge decision-aid system for wildland firefighting, powered by deep reinforcement learning. This innovative approach leverages rllib's hierarchical and multi-agent abstractions to recommend optimal fire suppression strategies based on complex environmental factors.
Dan Jacobson and John Cerillo demonstrate how their team composed a two-level hierarchical agent structure, mirroring real-world wildfire incident command. The system, trained using RLlib's multi-agent capabilities and scaled with Ray Core and Tune, has shown impressive results. In synthetically generated wildfire scenarios with varying wind and fuel conditions, the AI agents achieved full containment in 80% of incidents.
The presentation explores the intricacies of the wildfire management simulator, the training process using synthetic data, and future plans for refining the policy architecture. Jacobson and Cerillo also discuss their collection of historical wildfire incident data from across the continental United States, setting the stage for real-world evaluation and application of this groundbreaking AI-driven firefighting tool.
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Interested in more?
- Watch the full Day 1 Keynote: youtu.be/jwZHJthQvXo
- Watch the full Day 2 Keynote youtu.be/Lury2ad6KG8
- Check out the Ray Summmit Breakout sessions youtube.com/playlist?list=PLzTswPQNepXntmT8jr9WaNfqQ60QwW7-U&si=qPw-_SxT9lVmbRGE
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đź”— Connect with us:
- Subscribe to our YouTube channel: youtube.com/@anyscale
- Twitter: https://x.com/anyscalecompute
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- Website: anyscale.com
Lockheed Martin unveils its cutting-edge decision-aid system for wildland firefighting, powered by deep reinforcement learning. This innovative approach leverages rllib's hierarchical and multi-agent abstractions to recommend optimal fire suppression strategies based on complex environmental factors.
Dan Jacobson and John Cerillo demonstrate how their team composed a two-level hierarchical agent structure, mirroring real-world wildfire incident command. The system, trained using RLlib's multi-agent capabilities and scaled with Ray Core and Tune, has shown impressive results. In synthetically generated wildfire scenarios with varying wind and fuel conditions, the AI agents achieved full containment in 80% of incidents.
The presentation explores the intricacies of the wildfire management simulator, the training process using synthetic data, and future plans for refining the policy architecture. Jacobson and Cerillo also discuss their collection of historical wildfire incident data from across the continental United States, setting the stage for real-world evaluation and application of this groundbreaking AI-driven firefighting tool.
--
Interested in more?
- Watch the full Day 1 Keynote: youtu.be/jwZHJthQvXo
- Watch the full Day 2 Keynote youtu.be/Lury2ad6KG8
- Check out the Ray Summmit Breakout sessions youtube.com/playlist?list=PLzTswPQNepXntmT8jr9WaNfqQ60QwW7-U&si=qPw-_SxT9lVmbRGE
--
đź”— Connect with us:
- Subscribe to our YouTube channel: youtube.com/@anyscale
- Twitter: https://x.com/anyscalecompute
- LinkedIn: linkedin.com/company/joinanyscale
- Website: anyscale.com
