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Found 11 papers, 1 papers with code
Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs
We develop data-driven reinforcement learning (RL) control designs for input-affine nonlinear systems.
Robust and Scalable Game-theoretic Security Investment Methods for Voltage Stability of Power Systems
We develop investment approaches to secure electric power systems against load attacks where a malicious intruder (the attacker) covertly changes reactive power setpoints of loads to push the grid towards voltage instability while the system operator (the defender) employs reactive power compensation (RPC) to prevent instability.
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited information; (ii) issues on convergence or computational complexity emerge due to the curse of dimensionality.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph
In this work, we study MARLs with directed coordination graphs, and propose a distributed RL algorithm where the local policy evaluations are based on local value functions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Hierarchical Frequency and Voltage Control using Prioritized Utilization of Inverter Based Resources
We propose a novel hierarchical frequency and voltage control design for multi-area power system integrated with inverter-based resources (IBRs).
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL).
Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems based on Sample Identifying Complexity and Deciphering Time
Based on these two tractable new notions, we propose a systematic method for designing the both of an optimal key length to prevent system identification with a given precision within a given life span of systems, and of an optimal controller to maximize both of the control performance and the difficulty of the identification.
Decomposability and Parallel Computation of Multi-Agent LQR
Conditions for decomposability, an algorithm for constructing the transformation matrix, a parallel RL algorithm, and robustness analysis when the design is applied to non-homogeneous MAS are presented.
Hierarchical Reinforcement Learning
Reinforcement Learning (RL)
Model-Free Optimal Control of Linear Multi-Agent Systems via Decomposition and Hierarchical Approximation
The first component optimizes the performance of each independent cluster by solving the smaller-size LQR design problem in a model-free way using an RL algorithm.
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations
We present a set of model-free, reduced-dimensional reinforcement learning (RL) based optimal control designs for linear time-invariant singularly perturbed (SP) systems.
Efficient Randomized Algorithms for Subspace System Identification
Eigensystem Realization Algorithm (ERA) is a data-driven approach for subspace system identification and is widely used in many areas of engineering.
Numerical Analysis Numerical Analysis
