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Found 16 papers, 6 papers with code
Robust Online Voltage Control with an Unknown Grid Topology
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability.
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits their applicability for the control of safety-critical systems.
Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees
We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence.
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids.
Polymatrix Competitive Gradient Descent
In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents.
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by computing a global bound on its Lipschitz constant.
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training
Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges.
Understanding the Safety Requirements for Learning-based Power Systems Operations
Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems.
End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals.
Stable Online Control of Linear Time-Varying Systems
In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost.
Multi-Agent Reinforcement Learning in Cournot Games
This is the first result (to the best of our knowledge) on the convergence property of learning algorithms with continuous action spaces that do not fall in the no-regret class.
Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
Robust Reinforcement Learning for Continuous Control with Model Misspecification
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.
Product Review Summarization by Exploiting Phrase Properties
The main idea of our method is to leverage phrase properties to choose a subset of optimal phrases for generating the final summary.
