Local Forward Model Learning for Sample-Efficient Sequential Decision Making in Open World 3D Games

开放世界 3D 游戏中样本高效顺序决策的局部前向模型学习

• 批准号: 2441688
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2441688
• 关键词: Local; Forward; Model; Learning; Sample

Video games have been a platform for state of the art machine learning algorithms for some time now. Having a custom virtual environment provides us with the possibility to develop more and more complex algorithms to tackle practical life problems one step at a time. The proposed study will use MALMO as a platform to make sample efficient learning models and implement statistical forward planning models in a complex 3D partially observable game like Minecraft. An efficient learned local model, combined with Statistical Forward Planning methods that have been successful in various 2D games would allow the transition to the 3D environment which would breach the current barrier and bring us one step closer to the goal of creating general artificial intelligence.

一段时间以来，视频游戏一直是最先进的机器学习算法的平台。拥有一个自定义的虚拟环境为我们提供了开发越来越复杂的算法的可能性，以一步一步地解决实际生活中的问题。该研究将使用马尔莫作为平台，在像Minecraft这样的复杂3D部分可观察游戏中制作样本有效的学习模型并实现统计前瞻性规划模型。一个有效的学习局部模型，结合在各种2D游戏中成功的统计前瞻性规划方法，将允许过渡到3D环境，这将突破当前的障碍，使我们更接近创建通用人工智能的目标。