Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments
Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments
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DOI：
10.1115/1.4046293
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
C. López;James Cunningham;O. Ashour;Conrad S. Tucker
中科院分区：
文献类型：
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作者：
C. López;James Cunningham;O. Ashour;Conrad S. Tucker
这项工作提出了一种用于程序内容生成（PCG）的深度强化学习（DRL）方法，以自动生成用户可以与之交互的三维（3D）虚拟环境。PCG方法的主要目标是通过算法生成新内容，以改善用户体验。研究人员已经开始探索使用机器学习（ML）方法来生成内容。然而，这些方法经常实现监督ML算法，这些算法需要初始数据集来训练其生成模型。相比之下，RL算法不需要先验地收集训练数据，因为它们利用模拟来训练它们的模型。考虑到强化学习算法的优点，本文提出了一种通过在三维仿真平台上训练强化学习智能体来生成新的三维虚拟环境的方法。这项工作扩展了作者以前的工作，并提出了一个案例研究的结果，支持所提出的方法来生成新的三维虚拟环境的能力。自动生成新内容的能力有可能保持用户对各种应用程序的参与，例如用于教育和培训的虚拟现实应用程序以及工程概念设计。
This work presents a deep reinforcement learning (DRL) approach for procedural content generation (PCG) to automatically generate three-dimensional (3D) virtual environments that users can interact with. The primary objective of PCG methods is to algorithmically generate new content in order to improve user experience. Researchers have started exploring the use of machine learning (ML) methods to generate content. However, these approaches frequently implement supervised ML algorithms that require initial datasets to train their generative models. In contrast, RL algorithms do not require training data to be collected a priori since they take advantage of simulation to train their models. Considering the advantages of RL algorithms, this work presents a method that generates new 3D virtual environments by training an RL agent using a 3D simulation platform. This work extends the authors’ previous work and presents the results of a case study that supports the capability of the proposed method to generate new 3D virtual environments. The ability to automatically generate new content has the potential to maintain users’ engagement in a wide variety of applications such as virtual reality applications for education and training, and engineering conceptual design.