  We start by discussing the link between ecosystem simulators and general AI.
Then we present the open-source ecosystem simulator Ecotwin, which is based on
the game engine Unity and operates on ecosystems containing inanimate objects
like mountains and lakes, as well as organisms such as animals and plants.
Animal cognition is modeled by integrating three separate networks: (i) a
reflex network for hard-wired reflexes; (ii) a happiness network that maps
sensory data such as oxygen, water, energy, and smells, to a scalar happiness
value; and (iii) a policy network for selecting actions. The policy network is
trained with reinforcement learning (RL), where the reward signal is defined as
the happiness difference from one time step to the next. All organisms are
capable of either sexual or asexual reproduction, and they die if they run out
of critical resources. We report results from three studies with Ecotwin, in
which natural phenomena emerge in the models without being hardwired. First, we
study a terrestrial ecosystem with wolves, deer, and grass, in which a
Lotka-Volterra style population dynamics emerges. Second, we study a marine
ecosystem with phytoplankton, copepods, and krill, in which a diel vertical
migration behavior emerges. Third, we study an ecosystem involving lethal
dangers, in which certain agents that combine RL with reflexes outperform pure
RL agents.
