Title: Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation,
Abstract: Infants are experts at playing, with an amazing ability to generate novel
structured behaviors in unstructured environments that lack clear extrinsic
reward signals. We seek to replicate some of these abilities with a neural
network that implements curiosity-driven intrinsic motivation. Using a simple
but ecologically naturalistic simulated environment in which the agent can move
and interact with objects it sees, the agent learns a world model predicting
the dynamic consequences of its actions. Simultaneously, the agent learns to
take actions that adversarially challenge the developing world model, pushing
the agent to explore novel and informative interactions with its environment.
We demonstrate that this policy leads to the self-supervised emergence of a
spectrum of complex behaviors, including ego motion prediction, object
attention, and object gathering. Moreover, the world model that the agent
learns supports improved performance on object dynamics prediction and
localization tasks. Our results are a proof-of-principle that computational
models of intrinsic motivation might account for key features of developmental
visuomotor learning in infants.