Título: A study on handling intrinsic motivation for devising sample efficient actor-critic agents
Autores: André Quadros, Roberto Xavier Junior, Kleber Souza, Bruno Gomes, Filipe Saraiva and Ronnie Alves.
Resumo:
Reinforcement learning has evolved in recent years,and overcoming challenges found in this field. This area, unlikeconventional machine learning, does not learn through a setof observational instances, but through interaction with anenvironment. The sampling efficiency of a reinforcement learningagent is a challenge. That is, how to make an agent learn withinan environment with as little interaction as possible. In this workwe perform an experimental study on the difficulties to integratea strategy of intrinsic motivation to an actor-critic agent toimprove the sampling efficiency. We found results that point to theeffectiveness of the intrinsic motivation as a approach to improvethe agent’s sampling efficiency, as well as its performance. Weshare practical guidelines to assist in the implementation of actor-critic agents to deal with sparse reward environments whilemaking use of intrinsic motivation feedback.
Palavras-chave:
Reinforcement Learning, Intrinsic Motivation, Variational AutoEncoder.
Páginas: 8
Código DOI: 10.21528/CBIC2021-102
Artigo em pdf: CBIC_2021_paper_102.pdf
Arquivo BibTeX: CBIC_2021_102.bib