  We present an AI-based ecosystem simulator that uses three-dimensional models
of the terrain and animal models controlled by deep reinforcement learning. The
simulations take place in a game engine environment, which enables continuous
visual observation of the ecosystem model. The terrain models are generated
from geographic data with altitudes and land cover type. The animal models
combine three-dimensional conformation models with animation schemes and
decision-making mechanisms trained with deep reinforcement learning in
increasingly complex environments (curriculum learning). We show how AI tools
of this kind can be used for modeling the development of specific ecosystems
with and without different forms of economic activities. In particular, we show
how they might be used for modeling local biodiversity effects of land cover
change, exploitation of natural resources, pollution, invasive species, and
climate change.
