An Autonomous Emotional Virtual Character: An Approach with Deep and Goal-Parameterized Reinforcement Learning

Authors

  • Gilzamir Ferreira Gomes Universidade Federal do Ceará
  • Creto Augusto Vidal Universidade Federal do Ceará (UFC)
  • Joaquim Bento Cavalcante Neto Universidade Federal do Ceará (UFC)
  • Yuri Lenon Barbosa Nogueira Universidade Federal do Ceará

DOI:

https://doi.org/10.5753/jis.2020.751

Keywords:

Autonomous Virtual Characters, Emotion, Motivation, Deep Reinforcement Learning

Abstract

We have developed an autonomous virtual character guided by emotions. The agent is a virtual character who lives in a three-dimensional maze world. We found that emotion drivers can induce the behavior of a trained agent. Our approach is a case of goal parameterized reinforcement learning. Thus, we create conditioning between emotion drivers and a set of goals that determine the behavioral profile of a virtual character. We train agents who can randomly assume these goals while trying to maximize a reward function based on intrinsic and extrinsic motivations. A mapping between motivation and emotion was carried out. So, the agent learned a behavior profile as a training goal. The developed approach was integrated with the Advantage Actor-Critic (A3C) algorithm. Experiments showed that this approach produces behaviors consistent with the objectives given to agents, and has potential for the development of believable virtual characters.

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References

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Published

2020-10-09

How to Cite

GOMES, G. F.; VIDAL, C. A.; CAVALCANTE NETO, J. B.; NOGUEIRA, Y. L. B. An Autonomous Emotional Virtual Character: An Approach with Deep and Goal-Parameterized Reinforcement Learning. Journal on Interactive Systems, Porto Alegre, RS, v. 11, n. 1, p. 27–44, 2020. DOI: 10.5753/jis.2020.751. Disponível em: https://sol.sbc.org.br/journals/index.php/jis/article/view/751. Acesso em: 17 jan. 2021.

Issue

Section

Regular Paper