DRAS-CQSim: A reinforcement learning based framework for HPC cluster scheduling
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Fan, Yuping (ORCID:0000000294252780)↗
SEARCH · Engineering Papers
Results for “reinforcement Learning”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Cooperative bus holding and stop-skipping: A deep reinforcement learning framework
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Transportation↗
The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach
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Liu, Zeyu (ORCID:0000000305622661)↗
Molecular Autonomous Pathfinder Using Deep Reinforcement Learning
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Quantitative analysis of EXAFS data sets using deep reinforcement learning
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Science & Technology - Other Topics↗
Physics-informed models of domain wall dynamics as a route for autonomous domain wall design via reinforcement learning
Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.
42 ENGINEERING↗
Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
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71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control
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Mansourlakouraj, Mohammad↗
Efficient DER Voltage Control Using Ensemble Deep Reinforcement Learning.
Abstract not provided.
Obert, James↗
Reinforcement Learning for Charged Particle Beam Control to Minimize Injection Mismatch in Particle Accelerators
Explore the source record for details and available documents.
97 MATHEMATICS AND COMPUTING↗
Using Reinforcement Learning to Increase Grid Security Under Contingency Conditions.
Abstract not provided.
Verzi, Stephen↗
Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning
In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model.
Netter, Josh [Georgia Institute of Technology, Atl↗
Deep Reinforcement Learning For Online Distribution Power System Cybersecurity Protection.
Abstract not provided.
Bailey, Tyson↗
Explainable AI and Robustness-Based Test and Evaluation of Reinforcement Learning
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Raz, Ali K.↗
Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework
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Automation & Control Systems↗
Resilient Operation of Distribution Grids Using Deep Reinforcement Learning
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Automation & Control Systems↗