Neuro-evolutionary Frameworks for Generalized Learning Agents
  The recent successes of deep learning and deep reinforcement learning have
firmly established their statuses as state-of-the-art artificial learning
techniques. However, longstanding drawbacks of these approaches, such as their
poor sample efficiencies and limited generalization capabilities point to a
need for re-thinking the way such systems are designed and deployed. In this
paper, we emphasize how the use of these learning systems, in conjunction with
a specific variation of evolutionary algorithms could lead to the emergence of
unique characteristics such as the automated acquisition of a variety of
desirable behaviors and useful sets of behavior priors. This could pave the way
for learning to occur in a generalized and continual manner, with minimal
interactions with the environment. We discuss the anticipated improvements from
such neuro-evolutionary frameworks, along with the associated challenges, as
well as its potential for application to a number of research areas.
