Multi-Context Generation in Virtual Reality Environments Using Deep Reinforcement Learning
Multi-Context Generation in Virtual Reality Environments Using Deep Reinforcement Learning
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DOI：
10.1115/detc2020-22624
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发表时间：
2020-08
期刊：
影响因子：
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通讯作者：
James Cunningham;C. López;O. Ashour;Conrad S. Tucker
中科院分区：
文献类型：
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作者：
James Cunningham;C. López;O. Ashour;Conrad S. Tucker
在这项工作中，提出了一种用于程序内容生成（PCG）的深度强化学习（RL）方法，旨在自动生成多个相关的虚拟现实（VR）环境，以增强个性化学习。这允许用户暴露于展示一致主题的多个虚拟场景，这在教育环境中特别有价值。与采用监督学习方法的其他PCG方法相比，PCG的RL方法具有不需要训练数据的优点。这项工作通过展示产生多种背景以教授相同的基本概念的能力，推进了基于RL的PCG的最新技术。一个案例研究表明，建议的RL为基础的PCG方法的可行性，在制造设施和杂货店虚拟环境中的概率分布的例子。在本文中所展示的方法有可能使自动生成的各种虚拟环境，连接一个共同的概念或主题。
In this work, a Deep Reinforcement Learning (RL) approach is proposed for Procedural Content Generation (PCG) that seeks to automate the generation of multiple related virtual reality (VR) environments for enhanced personalized learning. This allows for the user to be exposed to multiple virtual scenarios that demonstrate a consistent theme, which is especially valuable in an educational context. RL approaches to PCG offer the advantage of not requiring training data, as opposed to other PCG approaches that employ supervised learning approaches. This work advances the state of the art in RL-based PCG by demonstrating the ability to generate a diversity of contexts in order to teach the same underlying concept. A case study is presented that demonstrates the feasibility of the proposed RL-based PCG method using examples of probability distributions in both manufacturing facility and grocery store virtual environments. The method demonstrated in this paper has the potential to enable the automatic generation of a variety of virtual environments that are connected by a common concept or theme.