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

• Autor(a) principal: Gomes, Gilzamir Ferreira
• Data de Publicação: 2020
• Outros Autores: Vidal, Creto Augusto, Cavalcante Neto, Joaquim Bento, Nogueira, Yuri Lenon Barbosa
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal on Interactive Systems
• Texto Completo: https://sol.sbc.org.br/journals/index.php/jis/article/view/751
• topic: Autonomous Virtual Characters; Emotion; Motivation; Deep Reinforcement Learning

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.