| Autor: |
James Cunningham, Omar Ashour, Conrad S. Tucker, Christian E. Lopez |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Volume 9: 40th Computers and Information in Engineering Conference (CIE). |
| DOI: |
10.1115/detc2020-22624 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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