The Ecosystem Path to General AI
Preprint, 2021

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.

ecosystem · neural networks · happiness · reflexes · reinforcement learning


Claes Strannegård

Data Science och AI

Niklas Engsner

Chalmers, Data- och informationsteknik, Data Science

Pietro Ferrari

Student vid Chalmers

Hans Glimmerfors

Student vid Chalmers

Marcus Hilding Södergren

Student vid Chalmers

Tobias Karlsson

Data Science och AI 1

Birger Kleve

Student vid Chalmers

Victor Skoglund

Student vid Chalmers


Informations- och kommunikationsteknik


Hållbar utveckling



Datavetenskap (datalogi)

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