Two Level Control of Non-Player Characters for Navigation in 3D Games Scenes: A Deep Reinforcement Learning Approach - Gomes, Gilzamir; Vidal, Creto A.; Cavalcante-Neto, Joaquim B.; Nogueira, Yuri L. B. (Proceedings of the Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), 2021)
This paper presents a deep reinforcement learning approach for navigation problem coupled with traditional animation processes in games. Deep Reinforcement Learning (DRL) is a promising approach for this problem. So, we design a NonPlayer Character (NPC) as an autonomous agent guided by a neural controller. Our approach works with the control of virtual game characters at two different levels of abstraction. A neural controller produces high-level actions, which are performed by both a character controller and an animation controller. We test our approach on a three-dimensional game scene. We find that our approach achieves promising results in navigation of animated characters in a game scene.