Search for: All records

Abstract 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. 

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. 

The objective of this work is to present an initial investigation of the impact the Connected Learning and Integrated Course Knowledge (CLICK) approach has had on students’ motivation, engineering identity, and learning outcomes. CLICK is an approach that leverages Virtual Reality (VR) technology to provide an integrative learning experience in the Industrial Engineering (IE) curriculum. To achieve this integration, the approach aims to leverage VR learning modules to simulate a variety of systems. The VR learning modules offer an immersive experience and provide the context for real-life applications. The virtual simulated system represents a theme to transfer the system concepts and knowledge across multiple IE courses as well as connect the experience with real-world applications. The CLICK approach has the combined effect of immersion and learning-by-doing benefits. In this work, VR learning modules are developed for a simulated manufacturing system. The modules teach the concepts of measures of location and dispersion, which are used in an introductory probability course within the IE curriculum. This work presents the initial results of comparing the motivation, engineering identity, and knowledge gain between a control and an intervention group (i.e., traditional vs. CLICK teaching groups). The CLICK approach group showed greater motivation compared to a traditional teaching group. However, there were no effects on engineering identity and knowledge gain. Nevertheless, it is hypothesized that the VR learning modules will have a positive impact on the students’ motivation, engineering identity, and knowledge gain over the long run and when used across the curriculum. Moreover, IE instructors interested in providing an immersive and integrative learning experience to their students could leverage the VR learning modules developed for this project. 

This work presents a Procedural Content Generation (PCG) method based on a Neural Network Reinforcement Learning (RL) approach that generates new environments for Virtual Reality (VR) learning applications. The primary objective of PCG methods is to algorithmically generate new content (e.g., environments, levels) in order to improve user experience. Researchers have started exploring the integration of Machine Learning (ML) algorithms into their PCG methods. These ML approaches help explore the design space and generate new content more efficiently. The capability to provide users with new content has great potential for learning applications. However, these ML algorithms require large datasets to train their generative models. In contrast, RL based methods do not require any training data to be collected a priori since they take advantage of simulation to train their models. Moreover, even though VR has become an emerging technology to engage users, there have been few studies that explore PCG for learning purposes and fewer in the context of VR. Considering these limitations, this work presents a method that generates new VR environments by training an RL in a simulation platform. This PCG method has the potential to maintain users’ engagement over time by presenting them with new environments in VR learning applications.