Cover Image

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Yang, Ma, Wenjie, Bu, Fanjin, Yang, Zhen, Wang, Bin, Han, Meng
Format:	Preprint
Published:	2023
Subjects:	Systems and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
by: Li, Yang, et al.
Published: (2023)

Representation Learning Enhanced Deep Reinforcement Learning for Optimal Operation of Hydrogen-based Multi-Energy Systems
by: Pu, Zhenyu, et al.
Published: (2026)

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
by: Li, Yuanzheng, et al.
Published: (2022)

Structure-Enhanced Deep Reinforcement Learning for Optimal Transmission Scheduling
by: Chen, Jiazheng, et al.
Published: (2022)

Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets
by: Li, Jinhao, et al.
Published: (2024)

RL-ADN: A High-Performance Deep Reinforcement Learning Environment for Optimal Energy Storage Systems Dispatch in Active Distribution Networks
by: Hou, Shengren, et al.
Published: (2024)

Semantic-aware Transmission Scheduling: a Monotonicity-driven Deep Reinforcement Learning Approach
by: Chen, Jiazheng, et al.
Published: (2023)

Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Scheduling
by: Zhao, Cunzhi, et al.
Published: (2023)

Robust Bandwidth Estimation for Real-Time Communication with Offline Reinforcement Learning
by: Kai, Jian, et al.
Published: (2025)

Topology-Aware Graph Reinforcement Learning for Energy Storage Systems Optimal Dispatch in Distribution Networks
by: Gao, Shuyi, et al.
Published: (2026)

Deep Reinforcement Learning Behavioral Mode Switching Using Optimal Control Based on a Latent Space Objective
by: Remman, Sindre Benjamin, et al.
Published: (2024)

Improving Mixed-Criticality Scheduling with Reinforcement Learning
by: El-Mahdy, Muhammad, et al.
Published: (2025)

Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control
by: Timmerman, Mike, et al.
Published: (2024)

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

A Comparative Study of Deep Reinforcement Learning for Crop Production Management
by: Balderas, Joseph, et al.
Published: (2024)

Cross-fitted Proximal Learning for Model-Based Reinforcement Learning
by: Venkatesh, Nishanth, et al.
Published: (2026)

Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach
by: Ramadan, Mohammad S., et al.
Published: (2023)

Nuclear Microreactor Control with Deep Reinforcement Learning
by: Tunkle, Leo, et al.
Published: (2025)

Decentralized Coordination of Distributed Energy Resources through Local Energy Markets and Deep Reinforcement Learning
by: May, Daniel, et al.
Published: (2024)

Can Optimal Transport Improve Federated Inverse Reinforcement Learning?
by: Millard, David, et al.
Published: (2026)

Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning
by: Sumayli, Mohammed, et al.
Published: (2025)

Kernel-Based Safe Exploration in Deep Reinforcement Learning
by: Majumdar, Rupak, et al.
Published: (2026)

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule
by: Huang, Yilie, et al.
Published: (2026)

Context-aware Constrained Reinforcement Learning Based Energy-Efficient Power Scheduling for Non-stationary XR Data Traffic
by: Wang, Kexuan, et al.
Published: (2025)

Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency
by: Zhang, Qixin, et al.
Published: (2025)

Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
by: Bahi, Abderaouf, et al.
Published: (2025)

A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management
by: Liu, M. Vivienne, et al.
Published: (2023)

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
by: Fu, Rong, et al.
Published: (2026)

Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control
by: Schäfer, Georg, et al.
Published: (2025)

Personalized Federated Learning by Energy-Efficient UAV Communications
by: Guo, Shiqian, et al.
Published: (2026)

The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning
by: Baheri, Ali, et al.
Published: (2024)

Feasible Policy Iteration for Safe Reinforcement Learning
by: Yang, Yujie, et al.
Published: (2023)

Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls
by: Hamilton, Nathaniel, et al.
Published: (2024)

Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of Beamlines
by: Wang, Siyu, et al.
Published: (2024)

Enhancing Battery Storage Energy Arbitrage with Deep Reinforcement Learning and Time-Series Forecasting
by: Sage, Manuel, et al.
Published: (2024)

Attentive Convolutional Deep Reinforcement Learning for Optimizing Solar-Storage Systems in Real-Time Electricity Markets
by: Li, Jinhao, et al.
Published: (2024)

Provably Optimal Reinforcement Learning under Safety Filtering
by: Oh, Donggeon David, et al.
Published: (2025)

Intent-Context Synergy Reinforcement Learning for Autonomous UAV Decision-Making in Air Combat
by: Fu, Jiahao, et al.
Published: (2026)

Optimal Batch-Size Control for Low-Latency Federated Learning with Device Heterogeneity
by: Yang, Huiling, et al.
Published: (2025)

The Pump Scheduling Problem: A Real-World Scenario for Reinforcement Learning
by: Donâncio, Henrique, et al.
Published: (2022)