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Multi-Agent Reinforcement Learning for Autonomous UAV Exploration in Wildfire Response
Caden Chandra, Jerry Ng
TL;DR
Wildfire monitoring requires autonomous systems that can adapt to rapidly changing conditions and limitations in conventional response and control methods. The study trains multi-agent DRL UAVs with reward shaping across simulated fire scenarios, finding more stable convergence and safer, more structured navigation, including fire-boundary tracking. The results support DRL UAVs as a promising basis for autonomous wildfire monitoring while retaining important simulation and causality limitations.
Problem
Rapidly changing wildfire conditions, operational constraints, and the computational and adaptability limits of conventional approaches create a need for more autonomous wildfire-monitoring systems.
Method
The study trains multi-agent DRL UAVs across simulated wildfire scenarios and tests how reward shaping and environmental complexity affect convergence and navigation behavior.
Results
Reward formulations emphasizing coverage efficiency and boundary tracking produced more stable learning curves, faster convergence, and higher average rewards, with average distance to fire decreasing from 7.5 units to 1.0.
Takeaways & Limitations
The findings indicate that DRL-driven UAV systems can develop safer, more structured navigation behaviors for simulated wildfire monitoring.
Takeaways & Limitations
Simulation omits wind, uneven terrain, sensor noise, and communication delays, and the observed performance relationships do not establish that any single training component caused the improvements.
Abstract
from arXiv · showhide
This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfire environments. Results show that agents learn increasingly stable and effective behaviors over time, as demonstrated by converging loss trends, improved reward signals, and more consistent navigation patterns such as fire-boundary tracking. Overall, these findings highlight the potential of deep reinforcement learning (DRL) based UAV systems for autonomous wildfire monitoring and suggest that environmental structure and reward design influence policy effectiveness.
1 Introduction
Wildfire response requires adaptive autonomous monitoring because dynamic fire conditions, operational constraints, and human oversight limit conventional approaches. The study tests whether reward shaping and environmental complexity influence multi-agent DRL UAV behavior across simulated fire scenarios.
- Motivation and study aim: Traditional rule-based methods and model predictive control struggle with rapidly changing fire conditions, while MPC also requires scenario-specific solutions and higher computational cost than reinforcement learning.Wildfire boundaries shift with wind and terrain, forcing UAVs to balance safety, exploration, and energy efficiency.
- Motivation and study aim: Human-piloted UAVs support hotspot detection and suppression but remain limited by operator dependence, reducing operational uptime and scalability.Recent AI-enabled UAV research therefore targets more autonomous wildfire-management strategies.
- Motivation and study aim: Bandwidth constraints and continued human oversight motivate hierarchical multi-UAV platforms, low-computation collaborative learning, and shared fire mapping.These initiatives aim to sustain fire coverage and improve onboard detection and mapping.
- Motivation and study aim: Reward shaping and environmental complexity jointly govern UAV convergence, adaptive navigation, and the influence of individual reward components in multi-agent wildfire simulations.The study tests this hypothesis across continuous fire fronts, spotting-driven jump fires, and dispersed ignition clusters.
- Study findings: Reward formulations emphasizing coverage efficiency and boundary tracking produced more stable learning curves, faster convergence, and higher average rewards, while average distance to fire decreased from 7.5 units to 1.0.Agents learned to patrol fire perimeters, reposition as conditions evolved, and maintain safe distances.
2 Process
The process models six wildfire configurations in a bounded three-dimensional environment and trains multi-agent UAVs with structured observations, shaped rewards, exploration noise, curriculum progression, and critic-based candidate selection. Reward components combine proximity, exploration, boundary, energy, and movement considerations to guide safe and purposeful navigation.
- Environment and scenarios: Six scenarios represent static, spreading, line-based, randomly placed, impassable-wall, and large central fires for training and testing different UAV navigation demands.The scenarios include random spreading, eight-fire lines, random initial placements, crash risks, and central-fire patrol conditions.
- Environment and scenarios: The simulation occupies a 20 × 10 × 10-unit 3D space, modeling drones as 0.25-unit-radius spheres with simplified cubic collision bounds and fires as vertical cylinders.The simplified geometry supports collision detection between drones and fire obstacles.
- Environment and scenarios: Each episode initializes fire configuration, drone positions, and the reward grid, while each timestep updates fire spread, observations, and reward components from structured multi-agent state information.Observations include each drone’s position, fire presence below, the other drone’s position, and fire-related information.
- Reward design: Fire proximity rewards penalize collisions and danger-zone entry while rewarding an optimal distance, and exploration rewards regenerate visited-cell value while adding bonuses for discovering unseen fires.Collisions immediately terminate episodes, linking safety directly to task continuation.
- Reward design: The total timestep reward is a weighted sum of normalized proximity, exploration, and energy components, supplemented by edge penalties and movement bonuses or stationary penalties.Purposeful movement is rewarded above a 0.75-unit threshold, while energy cost is based on action-magnitude changes.
- Training process: Gaussian noise generates diverse candidate actions, which critics evaluate alongside simulated rewards, energy penalties, and smooth-control regularization during training.The actor’s base action is perturbed consistently rather than annealed, and multiple critics estimate long-term value.
- Training process: Curriculum learning gradually reduces the safety margin across five levels, increasing difficulty and encouraging finer control near hazardous regions.The safety margin is defined as base margin + max(0, (5 − c)) · 0.1, where c is the curriculum level.
- Training process: Experience is stored in replay buffers, while reward components are normalized online with Welford’s algorithm after a 2,000-transition normalization start and 20,000-step replay warm-up.The reported component weights are 1.5 for proximity, 1.2 for exploration, and 10−6 for energy.
3 Results
Across 1000-episode training runs, the multi-agent reinforcement learning framework learned increasingly stable wildfire-exploration behavior, with converging losses, improved task performance, closer fire tracking, and more structured trajectories. Curriculum design and reward components further shaped navigation and exploration outcomes across wildfire scenarios.
- Training dynamics: Actor loss decayed from 10^-1 to near 10^-4, while critic loss decreased from 10^-1 and stabilized near 6 × 10^-2.
- Training dynamics: After 400 episodes, success rate stabilized near 100%, while reward stabilized near 250,000 after episode 800.
- Fire tracking: Average distance to the nearest fire dropped from 7.5 units to about 1.5 after 200 episodes, with variability converging near 1 after 400 episodes.
- Navigation behavior: Later trajectories became longer and more structured, including extended arcs and repeated passes around the fire perimeter.
- Curriculum effects: Curriculum levels shifted drones from wall-hugging toward closer fire exploration and reduced early-training crashes.
- 3.1 Ablation Study: Reward ablations showed scenario-dependent trade-offs, with Exploration viewing 150 tiles in one scenario, Edge averaging 150 tiles in another, and Stationary penalty reaching 220 tiles in the final scenario.
4 Discussion
Training produced more stable, effective, and safety-aware UAV navigation across wildfire scenarios, while reward composition shaped exploration, fire detection, and policy stability.
- Across scenarios, agents learned to explore, adapt, and patrol wildfire environments with stable convergence, improved coverage, and safety-aware navigation.Training also increased episode rewards and durations, reduced boundary collisions, and increased movement over 1000 episodes.
- Actor and critic losses converged stably, with decreasing critic oscillations indicating that value estimates adjusted as the policy evolved.Episode length and success-rate trends also reflected improved collision avoidance and continued environmental monitoring.
- Reward shaping supported longer, more successful episodes by encouraging collision avoidance, structured exploration, and fire-boundary patrolling.The increasing reward function indicates that agents maintained viewing distance from fires while discovering new fires.
- Ablation results showed that reward components produced distinct navigation strategies, with spatial-diversity rewards encouraging broader exploration and proximity or balanced objectives improving fire detection.The study used systematic removal of reward signals to examine their effects on learning behavior and exploration efficiency.
- The baseline ranked near the top across all six scenarios for fires viewed and tiles visited, indicating balanced reward weights across diverse conditions.A unified policy also avoided the uncertainty and safety risks associated with real-time fire-scenario classification for policy switching.
5 Conclusion
The study concludes that reward shaping, curriculum progression, and coordinated multi-agent training produced structured wildfire-monitoring behaviors in simulation. It also emphasizes that realistic disturbances, broader scenarios, and additional evaluation are needed before judging generalization to real wildfire conditions.
- Reward shaping, curriculum progression, and coordinated multi-agent training enabled reliable strategies for detecting and avoiding wildfires.The evaluation captured distance to fire, fires viewed, episode length, and collisions, but additional measures could provide a more complete assessment.
- Simulation improvements do not guarantee real-wildfire performance because the environment omits wind, uneven terrain, sensor noise, and communication delays.The observed performance relationships also do not establish that any single training component was solely responsible for the improvements.
- Future work should test more realistic fire dynamics, environmental and communication disturbances, alternative reward structures, and other reinforcement-learning algorithms.Expanding scenario count and fire-shape diversity would strengthen assessment of generalizability.
- The trained agents developed safer and more structured navigation behaviors over training, supporting the potential of DRL-driven UAV systems for wildfire monitoring.The conclusion frames the study as a foundation for improving autonomous wildfire tracking and broader environmental-monitoring applications.