An Emotional Virtual Character: A Deep Learning Approach with Reinforcement Learning

  • Gilzamir Gomes UECE
  • Creto Vidal UFC
  • Joaquim Cavalcante-Neto UFC
  • Yuri Nogueira UFC

Resumo


We have developed an emotional agent based on reinforcement learning. The agent is a virtual character who lives in a three-dimensional maze world. Thus, we found that an emotional engine can induce the behavior of an agent after training. The training algorithm was the Asynchronous Advantage Actor-Critic (A3C). Experiments showed that the emotional engine and reinforcement learning produced coherent behaviors. This is an important step towards believable autonomous virtual characters.
Palavras-chave: Believable Virtual Characters, Emotion models, Deep Reinforcement Learning
Publicado
28/10/2019
GOMES, Gilzamir; VIDAL, Creto; CAVALCANTE-NETO, Joaquim; NOGUEIRA, Yuri. An Emotional Virtual Character: A Deep Learning Approach with Reinforcement Learning. In: SIMPÓSIO DE REALIDADE VIRTUAL E AUMENTADA (SVR), 21. , 2019, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 32-40.