Autonomous wildfire suppression using reinforcement learning and multi-drone coordination
A complete wildfire simulation system with AI-powered drone agents
2D visualization with realistic fire spread, wind effects, and terrain
Autonomous patrol, fire detection, and suppression with resource management
Deep Q-Network agents with dueling architecture and experience replay
Fleet management with shared intelligence and optimized resource allocation
Core functionality we actually built
Heat-based spread with wind influence, multiple intensity levels, and terrain-dependent burn rates
Patrol patterns, fire detection, water management, and emergency recharging behavior
Dueling DQN with CNN processing, prioritized experience replay, and multi-objective rewards
Coordinated fleet operations with different drone types (simple + RL) working together
Comprehensive metrics logging, training visualization, and real-time statistics
Manual drone control, fire spawning, wind changes, and real-time parameter adjustment
How we trained the AI with sophisticated reward engineering
Primary objective - extinguishing fires
Protecting vegetation from fire spread
Complete mission success bonus
Encourage movement towards fires
Encourage exploration of new areas
Moving out of bounds or dangerous areas
Encourage efficient time usage
Penalty for each new fire that appears
Penalty for each tree/vegetation burned
Single rule-based vs Multiple RL agents in similar settings
Traditional approach with fixed logic
AI-powered coordinated fleet
Agents learn from experience and improve over time
Balances fire suppression, efficiency, and safety
Drones work together with shared intelligence
Optimized water and energy usage patterns
Adapts to changing fire conditions and wind
More drones = better coverage and efficiency
Technologies we actually used
Core language
Deep learning
RL environment
3D visualization
Training plots
Numerical computing
RL algorithm
Spatial processing