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Found 11 papers, 0 papers with code
Centralized vs. Decentralized Multi-Agent Reinforcement Learning for Enhanced Control of Electric Vehicle Charging Networks
In this paper, we introduce a novel approach for distributed and cooperative charging strategy using a Multi-Agent Reinforcement Learning (MARL) framework.
Model-free Resilient Controller Design based on Incentive Feedback Stackelberg Game and Q-learning
In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4. 0, the surge in development brings forth unprecedented security challenges.
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach
In this paper, we address the complexity of clustering users in PFL, especially in dynamic networks, by introducing a dynamic Upper Confidence Bound (dUCB) algorithm inspired by the multi-armed bandit (MAB) approach.
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
The increasing trend in adopting electric vehicles (EVs) will significantly impact the residential electricity demand, which results in an increased risk of transformer overload in the distribution grid.
Collaborative Wideband Spectrum Sensing and Scheduling for Networked UAVs in UTM Systems
In this paper, we propose a data-driven framework for collaborative wideband spectrum sensing and scheduling for networked unmanned aerial vehicles (UAVs), which act as the secondary users to opportunistically utilize detected spectrum holes.
Combating Uncertainties in Wind and Distributed PV Energy Sources Using Integrated Reinforcement Learning and Time-Series Forecasting
Furthermore, the large-scale integration of distributed energy resources (such as PV systems) creates new challenges for energy management in microgrids.
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids
This paper presents a Reinforcement Learning (RL) based energy market for a prosumer dominated microgrid.
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning
Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers energy bills.
Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks
Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale.
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency.
Networking and Internet Architecture Signal Processing
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach
We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium.
