Cover Image

Power Grid Control with Graph-Based Distributed Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Fabrizio, Carlo, Losapio, Gianvito, Mussi, Marco, Metelli, Alberto Maria, Restelli, Marcello
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

State and Action Factorization in Power Grids
by: Losapio, Gianvito, et al.
Published: (2024)

Bridging Rested and Restless Bandits with Graph-Triggering: Rising and Rotting
by: Genalti, Gianmarco, et al.
Published: (2024)

Best Arm Identification for Stochastic Rising Bandits
by: Mussi, Marco, et al.
Published: (2023)

Online Dynamic Pricing of Complementary Products
by: Mussi, Marco, et al.
Published: (2025)

A Provably Efficient Option-Based Algorithm for both High-Level and Low-Level Learning
by: Drappo, Gianluca, et al.
Published: (2024)

Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs
by: Maran, Davide, et al.
Published: (2024)

Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting
by: Russo, Alessio, et al.
Published: (2024)

Inverse Reinforcement Learning with Sub-optimal Experts
by: Poiani, Riccardo, et al.
Published: (2024)

Interpetable Target-Feature Aggregation for Multi-Task Learning based on Bias-Variance Analysis
by: Bonetti, Paolo, et al.
Published: (2024)

Projection by Convolution: Optimal Sample Complexity for Reinforcement Learning in Continuous-Space MDPs
by: Maran, Davide, et al.
Published: (2024)

Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models
by: Russo, Alessio, et al.
Published: (2025)

Pure Exploration under Mediators' Feedback
by: Poiani, Riccardo, et al.
Published: (2023)

Autoregressive Bandits
by: Bacchiocchi, Francesco, et al.
Published: (2022)

Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning
by: Montenegro, Alessandro, et al.
Published: (2024)

Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach
by: Poiani, Riccardo, et al.
Published: (2024)

Policy Gradient with Active Importance Sampling
by: Papini, Matteo, et al.
Published: (2024)

Generalized Kernelized Bandits: A Novel Self-Normalized Bernstein-Like Dimension-Free Inequality and Regret Bounds
by: Metelli, Alberto Maria, et al.
Published: (2025)

A Refined Analysis of UCBVI
by: Drago, Simone, et al.
Published: (2025)

Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards
by: Mussi, Marco, et al.
Published: (2024)

Statistical Analysis of Policy Space Compression Problem
by: Molaei, Majid, et al.
Published: (2024)

Information Capacity Regret Bounds for Bandits with Mediator Feedback
by: Eldowa, Khaled, et al.
Published: (2024)

Learning Optimal Deterministic Policies with Stochastic Policy Gradients
by: Montenegro, Alessandro, et al.
Published: (2024)

Optimal Multi-Fidelity Best-Arm Identification
by: Poiani, Riccardo, et al.
Published: (2024)

Actor-Critic with Active Importance Sampling
by: Molaei, Majid, et al.
Published: (2026)

Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning
by: Salaorni, Davide, et al.
Published: (2025)

Parameterized Projected Bellman Operator
by: Vincent, Théo, et al.
Published: (2023)

Building surrogate models using trajectories of agents trained by Reinforcement Learning
by: Cestero, Julen, et al.
Published: (2025)

Learning Deterministic Policies with Policy Gradients in Constrained Markov Decision Processes
by: Montenegro, Alessandro, et al.
Published: (2025)

No-Regret Reinforcement Learning in Smooth MDPs
by: Maran, Davide, et al.
Published: (2024)

A Reinforcement Learning Approach for Optimal Control in Microgrids
by: Salaorni, Davide, et al.
Published: (2025)

Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information
by: Zamboni, Riccardo, et al.
Published: (2025)

Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
by: Cestero, Julen, et al.
Published: (2025)

Sharing Knowledge in Multi-Task Deep Reinforcement Learning
by: D'Eramo, Carlo, et al.
Published: (2024)

Reusing Trajectories in Policy Gradients Enables Fast Convergence
by: Montenegro, Alessandro, et al.
Published: (2025)

Towards Principled Unsupervised Multi-Agent Reinforcement Learning
by: Zamboni, Riccardo, et al.
Published: (2025)

Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning
by: Mutti, Mirco, et al.
Published: (2023)

"So, Tell Me About Your Policy...": Distillation of interpretable policies from Deep Reinforcement Learning agents
by: Dispoto, Giovanni, et al.
Published: (2025)

Imitation Learning as Return Distribution Matching
by: Lazzati, Filippo, et al.
Published: (2025)

Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
by: Lazzati, Filippo, et al.
Published: (2025)

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
by: Cestero, Julen, et al.
Published: (2025)