Affordance as general value function: A computational model
  General value functions (GVFs) in the reinforcement learning (RL) literature
are long-term predictive summaries of the outcomes of agents following specific
policies in the environment. Affordances as perceived action possibilities with
specific valence may be cast into predicted policy-relative goodness and
modelled as GVFs. A systematic explication of this connection shows that GVFs
and especially their deep learning embodiments (1) realize affordance
prediction as a form of direct perception, (2) illuminate the fundamental
connection between action and perception in affordance, and (3) offer a
scalable way to learn affordances using RL methods. Through an extensive review
of existing literature on GVF applications and representative affordance
research in robotics, we demonstrate that GVFs provide the right framework for
learning affordances in real-world applications. In addition, we highlight a
few new avenues of research opened up by the perspective of "affordance as
GVF", including using GVFs for orchestrating complex behaviors.
