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Found 11 papers, 4 papers with code
NLBAC: A Neural Ordinary Differential Equations-based Framework for Stable and Safe Reinforcement Learning
Reinforcement learning (RL) excels in applications such as video games and robotics, but ensuring safety and stability remains challenging when using RL to control real-world systems where using model-free algorithms suffering from low sample efficiency might be prohibitive.
Scalable Forward Reachability Analysis of Multi-Agent Systems with Neural Network Controllers
This paper addresses the problem of finding overapproximations of forward reachable sets for discrete-time uncertain multi-agent systems with distributed NNC architectures.
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic Approach
Reinforcement learning (RL) has demonstrated impressive performance in various areas such as video games and robotics.
Learning Robust State Observers using Neural ODEs (longer version)
Relying on recent research results on Neural ODEs, this paper presents a methodology for the design of state observers for nonlinear systems based on Neural ODEs, learning Luenberger-like observers and their nonlinear extension (Kazantzis-Kravaris-Luenberger (KKL) observers) for systems with partially-known nonlinear dynamics and fully unknown nonlinear dynamics, respectively.
Large-Scale Graph Reinforcement Learning in Wireless Control Systems
As the number of learnable parameters in a neural network grows with the size of the input signal, deep reinforcement learning may fail to scale, limiting the immediate generalization of such scheduling and resource allocation policies to large-scale systems.
Federated Reinforcement Learning at the Edge
Modern cyber-physical architectures use data collected from systems at different physical locations to learn appropriate behaviors and adapt to uncertain environments.
Linear Regression over Networks with Communication Guarantees
A key functionality of emerging connected autonomous systems such as smart cities, smart transportation systems, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations.
Adaptive Scheduling for Machine Learning Tasks over Networks
A key functionality of emerging connected autonomous systems such as smart transportation systems, smart cities, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations.
Model-Free Design of Control Systems over Wireless Fading Channels
Wireless control systems replace traditional wired communication with wireless networks to exchange information between actuators, plants and sensors in a control system.
Statistical Learning for Analysis of Networked Control Systems over Unknown Channels
In this regard our work is the first to characterize the amount of channel modeling that is required to answer such a question.
Cloud-based Quadratic Optimization with Partially Homomorphic Encryption
The development of large-scale distributed control systems has led to the outsourcing of costly computations to cloud-computing platforms, as well as to concerns about privacy of the collected sensitive data.
Optimization and Control Cryptography and Security Systems and Control
