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

by   Miguel Aguilera, et al.

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.


page 1

page 2

page 3

page 4


Adaptation to criticality through organizational invariance in embodied agents

Many biological and cognitive systems do not operate deep within one or ...

Learning Criticality in an Embodied Boltzmann Machine

Many biological and cognitive systems do not operate deep into one or ot...

DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting

Understanding human motion behaviour is a critical task for several poss...

Towards Social Identity in Socio-Cognitive Agents

Current architectures for social agents are designed around some specifi...

On the Importance of Critical Period in Multi-stage Reinforcement Learning

The initial years of an infant's life are known as the critical period, ...

Hard Attention Control By Mutual Information Maximization

Biological agents have adopted the principle of attention to limit the r...

Please sign up or login with your details

Forgot password? Click here to reset