Image de couverture de livre

An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Fernandez-Bes, Jesus, Cid-Sueiro, Jesus, Marques, Antonio G.
Format:	Preprint
Publié:	2025
Sujets:	Systems and Control Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Asymptotic analysis of cooperative censoring policies in sensor networks
par: Fernandez-Bes, Jesus, et autres
Publié: (2025)

Online Learning of Weakly Coupled MDP Policies for Load Balancing and Auto Scaling
par: Eshwar, S. R., et autres
Publié: (2024)

Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions
par: Za'ter, Muhy Eddin, et autres
Publié: (2025)

Identification For Control Based on Neural Networks: Approximately Linearizable Models
par: Thieffry, Maxime, et autres
Publié: (2024)

Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependencies
par: DeWeese, Alex, et autres
Publié: (2024)

On the Optimality of Uncertain MDP Abstractions
par: Gracia, Ibon, et autres
Publié: (2026)

Approximate Model Predictive Control for Microgrid Energy Management via Imitation Learning
par: Liu, Changrui, et autres
Publié: (2025)

Approximately Optimal Global Planning for Contact-Rich SE(2) Manipulation on a Graph of Reachable Sets
par: Liu, Simin, et autres
Publié: (2026)

Differentiable Optimization for Deep Learning-Enhanced DC Approximation of AC Optimal Power Flow
par: Rosemberg, Andrew, et autres
Publié: (2025)

Fuzzy Model Identification and Self Learning with Smooth Compositions
par: Sadjadi, Ebrahim Navid, et autres
Publié: (2024)

Intelligent Duty Cycling Management and Wake-up for Energy Harvesting IoT Networks with Correlated Activity
par: Ruíz-Guirola, David E., et autres
Publié: (2024)

Large Language Model as An Operator: An Experience-Driven Solution for Distribution Network Voltage Control
par: Yang, Xu, et autres
Publié: (2025)

Learning-Based Approximate Nonlinear Model Predictive Control Motion Cueing
par: Arango, Camilo Gonzalez, et autres
Publié: (2025)

Approximate Information States for Worst-Case Control and Learning in Uncertain Systems
par: Dave, Aditya, et autres
Publié: (2023)

Approximate Model-Based Shielding for Safe Reinforcement Learning
par: Goodall, Alexander W., et autres
Publié: (2023)

Optimal Power Grid Operations with Foundation Models
par: Puech, Alban, et autres
Publié: (2024)

Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation
par: Xie, Kedi, et autres
Publié: (2025)

Optimized Renewable Energy Planning MDP for Socially-Equitable Electricity Coverage in the US
par: Kinnarkar, Riya, et autres
Publié: (2025)

Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints
par: Zhou, Yuqi, et autres
Publié: (2024)

RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion
par: Kang, Dongho, et autres
Publié: (2023)

Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
par: Gallon, Riccardo, et autres
Publié: (2024)

Deep-MPC: A DAGGER-Driven Imitation Learning Strategy for Optimal Constrained Battery Charging
par: Espin, Jorge, et autres
Publié: (2024)

An Optimal Battery-Free Approach for Emission Reduction by Storing Solar Surplus in Building Thermal Mass
par: Boffi, Michela, et autres
Publié: (2026)

Dispatch-Aware Deep Neural Network for Optimal Transmission Switching: Toward Real-Time and Feasibility Guaranteed Operation
par: Kim, Minsoo, et autres
Publié: (2025)

Reinforcement Learning-Based Co-Design and Operation of Chiller and Thermal Energy Storage for Cost-Optimal HVAC Systems
par: Srinivasa, Tanay Raghunandan, et autres
Publié: (2026)

Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
par: Adibi, Arman, et autres
Publié: (2024)

Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management
par: Xu, Jinming, et autres
Publié: (2023)

Model Predictive Control of Hybrid Dynamical Systems
par: Sanfelice, Ricardo G., et autres
Publié: (2026)

Fitted Q-Iteration via Max-Plus-Linear Approximation
par: Liu, Y., et autres
Publié: (2024)

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
par: Skifstad, Julian, et autres
Publié: (2026)

Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptance
par: Katz, Sydney M., et autres
Publié: (2025)

Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
par: Ibrahim, Sinan, et autres
Publié: (2026)

Annealing Optimization for Progressive Learning with Stochastic Approximation
par: Mavridis, Christos, et autres
Publié: (2022)

Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks
par: Zhang, Hongchao, et autres
Publié: (2024)

Bayesian Optimization of Process Parameters of a Sensor-Based Sorting System using Gaussian Processes as Surrogate Models
par: Kronenwett, Felix, et autres
Publié: (2025)

A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems
par: Tayal, Manan, et autres
Publié: (2025)

Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
par: Zhu, Feng, et autres
Publié: (2025)

DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
par: Fabbro, Nicolò Dal, et autres
Publié: (2024)

Model-Free Learning and Optimal Policy Design in Multi-Agent MDPs Under Probabilistic Agent Dropout
par: Fiscko, Carmel, et autres
Publié: (2023)

Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves
par: Sarkar, Soumyendu, et autres
Publié: (2024)