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

The Ecosystem Path to General AI