Neuro-evolutionary Frameworks for Generalized Learning Agents

• URL: http://arxiv.org/abs/2002.01088v1
• Date: Tue, 4 Feb 2020 02:11:56 GMT
• Title: Neuro-evolutionary Frameworks for Generalized Learning Agents
• Authors: Thommen George Karimpanal
• Abstract summary: Recent successes of deep learning and deep reinforcement learning have firmly established their statuses as state-of-the-art artificial learning techniques. Longstanding drawbacks of these approaches point to a need for re-thinking the way such systems are designed and deployed. We discuss the anticipated improvements from such neuro-evolutionary frameworks, along with the associated challenges.
• Score: 1.2691047660244335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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