Neuro-evolutionary Frameworks for Generalized Learning Agents

TL;DR

This paper explores neuro-evolutionary frameworks that combine deep learning with evolutionary algorithms to enhance generalization, sample efficiency, and continual learning in artificial agents.

Contribution

It introduces a novel neuro-evolutionary approach aiming to improve generalization and continual learning in AI systems, addressing limitations of current deep learning methods.

Findings

Potential for automated acquisition of diverse behaviors

Enhanced generalization capabilities

Reduced environmental interactions for learning

Abstract

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.