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This paper outlines a methodological approach for designing adaptive agentsdriving themselves near points of criticality. Using a synthetic approach weconstruct a conceptual model that, instead of specifying mechanisticrequirements to generate criticality, exploits the maintenance of anorganizational structure capable of reproducing critical behavior. Our approachexploits the well-known principle of universality, which classifies criticalphenomena inside a few universality classes of systems independently of theirspecific mechanisms or topologies. In particular, we implement an artificialembodied agent controlled by a neural network maintaining a correlationstructure randomly sampled from a lattice Ising model at a critical point. Weevaluate the agent in two classical reinforcement learning scenarios: theMountain Car benchmark and the Acrobot double pendulum, finding that in bothcases the neural controller reaches a point of criticality, which coincideswith a transition point between two regimes of the agent's behaviour,maximizing the mutual information between neurons and sensorimotor patterns.Finally, we discuss the possible applications of this synthetic approach to thecomprehension of deeper principles connected to the pervasive presence ofcriticality in biological and cognitive systems.

Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents