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1. Deep Reinforcement Learning for Procedural Content Generation of 3D Virtual Environments

   https://doi.org/10.1115/1.4046293  

   López, Christian E.; Cunningham, James; Ashour, Omar; Tucker, Conrad S. (October 2020, Journal of Computing and Information Science in Engineering)

   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. 

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2. Multi-Context Generation in Virtual Reality Environments using Deep Reinforcement Learning

   Cunningham, James; Lopez, Christian; Ashour, Omar; Tucker, Conrad (August 2020, ASME IDETC-CIE)

   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. 

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3. User Identification Utilizing Minimal Eye-Gaze Features in Virtual Reality Applications

   https://doi.org/10.3390/virtualworlds1010004  

   Asish, Sarker Monojit; Kulshreshth, Arun K.; Borst, Christoph W. (December 2022, Virtual Worlds)

   Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work. 

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4. Reinforcement learning‐guided control strategies for CAR T‐cell activation and expansion

   https://doi.org/10.1002/bit.28753  

   Ferdous, Sakib; Shihab, Ibne_Farabi; Chowdhury, Ratul; Reuel, Nigel_F (May 2024, Biotechnology and Bioengineering)

   Abstract Reinforcement learning (RL), a subset of machine learning (ML), could optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR T‐cell activation by antigen‐presenting beads and their subsequent expansion is formulated in silico. The simulation is used as an environment to train RL‐agents to dynamically control the number of beads in culture to maximize the population of robust effector cells at the end of the culture. We make periodic decisions of incremental bead addition or complete removal. The simulation is designed to operate in OpenAI Gym, enabling testing of different environments, cell types, RL‐agent algorithms, and state inputs to the RL‐agent. RL‐agent training is demonstrated with three different algorithms (PPO, A2C, and DQN), each sampling three different state input types (tabular, image, mixed); PPO‐tabular performs best for this simulation environment. Using this approach, training of the RL‐agent on different cell types is demonstrated, resulting in unique control strategies for each type. Sensitivity to input‐noise (sensor performance), number of control step interventions, and advantages of pre‐trained RL‐agents are also evaluated. Therefore, we present an RL framework to maximize the population of robust effector cells in CAR T‐cell therapy production. 

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5. A Novel FPGA-Based Circuit Simulator for Accelerating Reinforcement Learning-Based Design of Power Converters

   https://doi.org/10.1109/ASAP57973.2023.00013  

   Xu, Zhenyu; Yu, Miaoxiang; Yang, Qing Yang; Jeong, Yeonho; Cai, Jillian; Wei, Tao (July 2023, 34th IEEE International Conference on Application-specific Systems, Architectures and Processors.)

   Abstract: High-efficiency energy conversion systems have become increasingly important due to their wide use in all electronic systems such as data centers, smart mobile devices, E-vehicles, medical instruments, and so forth. Complex and interdependent parameters make optimal designs of power converters challenging to get. Recent research has shown that machine learning (ML) algorithms, such as reinforcement learning (RL), show great promise in design of such converter circuits. A trained RL agent can search for optimal design parameters for power conversion circuit topologies under targeted application requirements. Training an RL agent requires numerous circuit simulations. It requires significantly more training iterations when the tolerance of circuit components due to manufacturing inconsistency, aging, and temperature variation is considered. As a result, they may take days to complete, primarily because of the slow time-domain circuit simulation. This paper proposes a new FPGA architecture that accelerates the circuit simulation and hence substantially speeds up the RL-based design method for power converters. Our new architecture supports all power electronic circuit converters and their variations. It substantially improves the training speed of RL-based design methods. High-level synthesis (HLS) was used to build the accelerator on Amazon Web Service (AWS) F1 instance. An AWS virtual PC hosts the training algorithm. The host interacts with the FPGA accelerator by updating the circuit parameters, initiating simulation, and collecting the simulation results during training iterations. A script was created on the host side to facilitate this design method to convert a netlist containing circuit topology and parameters into core matrices in the FPGA accelerator. Experimental results showed 60× overall speedup of our RL-based design method in comparison with using a popular commercial simulator, PowerSim. 

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