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Workshop: Ecological Theory of Reinforcement Learning: How Does Task Design Influence Agent Learning?

Grounding an Ecological Theory of Artificial Intelligence in Human Evolution

Eleni Nisioti · Clément Moulin-Frier


Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. Although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes and ecological conditions that may have guided the emergence of complex cognitive capacities during the evolution of the species.

Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in our ecological niche. In this paper, we propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL). We use this framework to highlight fundamental links between the two disciplines, as well as to identify feedback loops that bootstrap ecological complexity and create promising research directions for AI researchers. We also present our first steps towards designing a simulation environment that implements the climate dynamics necessary for studying key HBE hypotheses relating environmental complexity to skill acquisition.