Modelling Ecosystems with Deep Reinforcement Learning
I will present the ecosystem simulator Ecotwin (www.ecotwin.se), which uses three-dimensional terrain models based on real geographical data together with individual animal models controlled by deep reinforcement learning. Moreover, I will illustrate how this simulator can be used for studying pristine ecosystems as well as ecosystems affected by human economic activities such as exploitation of natural resources, land use change, pollution, and climate change. The main benefit of the simulator is that it can be used as an analytical tool at the planning stage for understanding local ecological consequences of a range of economic activities, thus ensuring relatively eco-friendly decision-making.
Claes Strannegård is an associate professor at the department of Applied IT at the University of Gothenburg.
