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1. Reinforcement Learning Content Generation for Virtual Reality Applications

   Lopez, Christian E; Ashour, Omar M; Tucker, Conrad (August 2019, ASME IDETC-CIE)

   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. 

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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. Efficient Parallel Reinforcement Learning Framework Using the Reactor Model

   https://doi.org/10.1145/3626183.3659967  

   Kwok, Jacky; Lohstroh, Marten; Lee, Edward A (June 2024, ACM)

   Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of values, and policy improvement. These computational paradigms require a seamless integration of training, serving, and simulation workloads. Existing frameworks, such as Ray, are not managing this orchestration efficiently, especially in RL tasks that demand intensive input/output and synchronization between actors on a single node. In this study, we have proposed a solution implementing the reactor model, which enforces a set of actors to have a fixed communication pattern. This allows the scheduler to eliminate work needed for synchronization, such as acquiring and releasing locks for each actor or sending and processing coordination-related messages. Our framework, Lingua Franca (LF), a coordination language based on the reactor model, also supports true parallelism in Python and provides a unified interface that allows users to automatically generate dataflow graphs for RL tasks. In comparison to Ray on a single-node multi-core compute platform, LF achieves 1.21x and 11.62x higher simulation throughput in OpenAI Gym and Atari environments, reduces the average training time of synchronized parallel Q-learning by 31.2%, and accelerates multi-agent RL inference by 5.12x. 

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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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