# Distributed Wildfire Surveillance with Autonomous Aircraft using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1810.04244 · HTML: https://mlsanity.com/paper/1810.04244/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2018.

## Built on by (most cited first, 17 of 17)

- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges](https://mlsanity.com/paper/2305.10091/) (2023)
- [Multi-UAV Planning for Cooperative Wildfire Coverage and Tracking with Quality-of-Service Guarantees](https://mlsanity.com/paper/2206.10544/) (2022)
- [Learning Continuous Control Policies for Information-Theoretic Active Perception](https://mlsanity.com/paper/2209.12427/) (2022)
- [Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control](https://mlsanity.com/paper/2103.04666/) (2021)
- [Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories](https://mlsanity.com/paper/2212.01498/) (2022)
- [ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments](https://mlsanity.com/paper/2405.20705/) (2024)
- [ROS-X-Habitat: Bridging the ROS Ecosystem with Embodied AI](https://mlsanity.com/paper/2109.07703/) (2021)
- [PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments](https://mlsanity.com/paper/2403.11095/) (2024)
- [Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning](https://mlsanity.com/paper/2203.10823/) (2022)
- [Learned spatial data partitioning](https://mlsanity.com/paper/2306.04846/) (2023)
- [Safety, Trust, and Ethics Considerations for Human-AI Teaming in Aerospace Control](https://mlsanity.com/paper/2311.08943/) (2023)
- [A Semi-Decentralized Approach to Multiagent Control](https://mlsanity.com/paper/2603.11802/) (2026)
- [Communication-Aware Multi-Agent Reinforcement Learning for Decentralized Cooperative UAV Deployment](https://mlsanity.com/paper/2603.16141/) (2026)
- [ED3R: Energy-Aware Distributed Disaster Detection via Cooperative Agents in Robotic Systems](https://mlsanity.com/paper/2606.17739/) (2026)
- [PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability](https://mlsanity.com/paper/2608.14082/) (2026)
- [D3ARC: Time-Critical Distributed Disaster Detection for Asynchronous Cooperative Multi-Robot Systems](https://mlsanity.com/paper/2609.07350/) (2026)

## Abstract (arXiv)

Teams of autonomous unmanned aircraft can be used to monitor wildfires, enabling firefighters to make informed decisions. However, controlling multiple autonomous fixed-wing aircraft to maximize forest fire coverage is a complex problem. The state space is high dimensional, the fire propagates stochastically, the sensor information is imperfect, and the aircraft must coordinate with each other to accomplish their mission. This work presents two deep reinforcement learning approaches for training decentralized controllers that accommodate the high dimensionality and uncertainty inherent in the problem. The first approach controls the aircraft using immediate observations of the individual aircraft. The second approach allows aircraft to collaborate on a map of the wildfire's state and maintain a time history of locations visited, which are used as inputs to the controller. Simulation results show that both approaches allow the aircraft to accurately track wildfire expansions and outperform an online receding horizon controller. Additional simulations demonstrate that the approach scales with different numbers of aircraft and generalizes to different wildfire shapes.