Title: Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents,
Abstract: This paper outlines a methodological approach for designing adaptive agents
driving themselves near points of criticality. Using a synthetic approach we
construct a conceptual model that, instead of specifying mechanistic
requirements to generate criticality, exploits the maintenance of an
organizational structure capable of reproducing critical behavior. Our approach
exploits the well-known principle of universality, which classifies critical
phenomena inside a few universality classes of systems independently of their
specific mechanisms or topologies. In particular, we implement an artificial
embodied agent controlled by a neural network maintaining a correlation
structure randomly sampled from a lattice Ising model at a critical point. We
evaluate the agent in two classical reinforcement learning scenarios: the
Mountain Car benchmark and the Acrobot double pendulum, finding that in both
cases the neural controller reaches a point of criticality, which coincides
with 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 the
comprehension of deeper principles connected to the pervasive presence of
criticality in biological and cognitive systems.