论文标题
高水平角色控制的深度强化学习
Deep Reinforcement Learning for High Level Character Control
论文作者
论文摘要
在本文中，我们建议在为计算媒体创建智能字符中使用传统动画，启发式行为和强化学习。传统的动画和启发式方法可以使行为进行艺术控制，同时加强学习增加了概括。提出的用例是在3D环境中具有高级控制器的狗角色，该环境围绕要学习的所需行为，例如获取项目。由于环境的开发是学习的关键，因此进一步分析了如何构建这些学习环境，环境和代理建模选择的影响，培训程序和学习行为的概括。该分析建立了对上述因素的见识，并可以作为一般环境发展的指导。
In this paper, we propose the use of traditional animations, heuristic behavior and reinforcement learning in the creation of intelligent characters for computational media. The traditional animation and heuristic gives artistic control over the behavior while the reinforcement learning adds generalization. The use case presented is a dog character with a high-level controller in a 3D environment which is built around the desired behaviors to be learned, such as fetching an item. As the development of the environment is the key for learning, further analysis is conducted of how to build those learning environments, the effects of environment and agent modeling choices, training procedures and generalization of the learned behavior. This analysis builds insight of the aforementioned factors and may serve as guide in the development of environments in general.
