The lab's ongoing research cover a range of problems in decision making and prediction for Forest Management. This includes algorithms for :
- optimization of sustainable harvest policies under spatial constraints using Reinforcement Learning and Policy Gradient Search.
- learning dynamics of forest fire spread from simulations and satellite data using Deep Reinforcement Learning
- generation of future fire spread scenarios from image series using Long-Term Recurrent Convolutional Neural Networks
- modelling of decision making about treatment against forest wildfires using Markov decision processes