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1. Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning

   https://doi.org/10.3390/en16020652  

   Yao, Zhengyu; Yoon, Hwan-Sik; Hong, Yang-Ki (January 2023, Energies)

   Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based control algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based powertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms. 

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2. The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way

   Sanz_Ausin, Markel; Maniktala, Mehak; Barnes, Tiffany; Chi, Min (April 2023, International journal of artificial intelligence in education)

   While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the \textit{human-agent interactions} productive and fruitful. In real-life, complex, human-centric tasks, such as education and healthcare, data can be noisy and limited. Batch RL is designed for handling such situations where data is \textit{limited yet noisy}, and where \textit{building simulations is challenging}. In two consecutive empirical studies, we investigated Batch DRL for pedagogical policy induction, to choose student learning activities in an Intelligent Tutoring System. In Fall 2018 (F18), we compared the Batch DRL policy to an Expert policy, but found no significant difference between the DRL and Expert policies. In Spring 2019 (S19), we augmented the Batch DRL-induced policy with \textit{a simple act of explanation} by showing a message such as \textit{"The AI agent thinks you should view this problem as a Worked Example to learn how some new rules work."}. We compared this policy against two conditions, the Expert policy, and a student decision making policy. Our results show that 1) the Batch DRL policy with explanations significantly improved student learning performance more than the Expert policy; and 2) no significant differences were found between the Expert policy and student decision making. Overall, our results suggest that \textit{pairing simple explanations with the Batch DRL policy} can be an important and effective technique for applying RL to real-life, human-centric tasks. 

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3. Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights

   https://doi.org/10.1109/ISGTEUROPE62998.2024.10863452  

   Shi, Di; Zhang, Qiang; Hong, Mingguo; Wang, Fengyu; Maslennikov, Slava; Luo, Xiaochuan; Chen, Yize (October 2024, IEEE)

   Deep reinforcement learning (DRL) holds significant promise for managing voltage control challenges in simulated power grid environments. However, its real-world application in power system operations remains underexplored. This study rigorously evaluates DRL’s performance and limitations within actual operational contexts by utilizing detailed experiments across the IEEE 14-bus system, Illinois 200-bus system, and the ISO New England node-breaker model. Our analysis critically assesses DRL’s effectiveness for grid control from a system operator's perspective, identifying specific performance bottlenecks. The findings provide actionable insights that highlight the necessity of advancing AI technologies to effectively address the growing complexities of modern power systems. This research underscores the vital role of DRL in enhancing grid management and reliability. 

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4. A Deep Reinforcement Learning Framework for Architectural Exploration: A Routerless NoC Case Study

   https://doi.org/10.1109/HPCA47549.2020.00018  

   Lin, Ting-Ru; Penney, Drew; Pedram, Massoud; Chen, Lizhong (February 2020, IEEE International Symposium on High Performance Computer Architecture (HPCA))

   null (Ed.)

   Machine learning applied to architecture design presents a promising opportunity with broad applications. Recent deep reinforcement learning (DRL) techniques, in particular, enable efficient exploration in vast design spaces where conventional design strategies may be inadequate. This paper proposes a novel deep reinforcement framework, taking routerless networks-on-chip (NoC) as an evaluation case study. The new framework successfully resolves problems with prior design approaches, which are either unreliable due to random searches or inflexible due to severe design space restrictions. The framework learns (near-)optimal loop placement for routerless NoCs with various design constraints. A deep neural network is developed using parallel threads that efficiently explore the immense routerless NoC design space with a Monte Carlo search tree. Experimental results show that, compared with conventional mesh, the proposed deep reinforcement learning (DRL) routerless design achieves a 3.25x increase in throughput, 1.6x reduction in packet latency, and 5x reduction in power. Compared with the state-of-the-art routerless NoC, DRL achieves a 1.47x increase in throughput, 1.18x reduction in packet latency, 1.14x reduction in average hop count, and 6.3% lower power consumption. 

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5. ACADIA: Efficient and Robust Adversarial Attacks Against Deep Reinforcement Learning

   https://doi.org/10.1109/CNS56114.2022.9947234  

   Ali, Haider; Al Ameedi, Mohannad; Swami, Ananthram; Ning, Rui; Li, Jiang; Wu, Hongyi; Cho, Jin-Hee (October 2022, IEEE Conference on Communications and Network Security} (CNS))

   Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial attacks, called ACADIA, representing AttaCks Against Deep reInforcement leArning. ACADIA provides a set of efficient and robust perturbation-based adversarial attacks to disturb the DRL agent's decision-making based on novel combinations of techniques utilizing momentum, ADAM optimizer (i.e., Root Mean Square Propagation, or RMSProp), and initial randomization. These kinds of DRL attacks with novel integration of such techniques have not been studied in the existing Deep Neural Networks (DNNs) and DRL research. We consider two well-known DRL algorithms, Deep-Q Learning Network (DQN) and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed ACADIA outperforms existing gradient-based counterparts under a wide range of experimental settings. ACADIA is nine times faster than the state-of-the-art Carlini & Wagner (CW) method with better performance under defenses of DRL. 

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