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

Keywords

• Autonomous Virtual Characters
• Emotion
• Motivation
• Deep Reinforcement Learning