Saved in:
| Main Authors: | Ali, Nawazish, Shaw, Rachael, Mason, Karl |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.08052 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization
by: Ali, Nawazish, et al.
Published: (2024)
by: Ali, Nawazish, et al.
Published: (2024)
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
by: Ali, Nawazish, et al.
Published: (2024)
by: Ali, Nawazish, et al.
Published: (2024)
Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms
by: Shah, Mian Ibad Ali, et al.
Published: (2024)
by: Shah, Mian Ibad Ali, et al.
Published: (2024)
Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
by: Shah, Mian Ibad Ali, et al.
Published: (2025)
by: Shah, Mian Ibad Ali, et al.
Published: (2025)
A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming
by: Shah, Mian Ibad Ali, et al.
Published: (2023)
by: Shah, Mian Ibad Ali, et al.
Published: (2023)
Uncertainty-Aware Knowledge Transformers for Peer-to-Peer Energy Trading with Multi-Agent Reinforcement Learning
by: Shah, Mian Ibad Ali, et al.
Published: (2025)
by: Shah, Mian Ibad Ali, et al.
Published: (2025)
Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control
by: Shianifar, Jonaid, et al.
Published: (2024)
by: Shianifar, Jonaid, et al.
Published: (2024)
Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey
by: Dong, Qi, et al.
Published: (2024)
by: Dong, Qi, et al.
Published: (2024)
Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning
by: Lu, Junlin, et al.
Published: (2024)
by: Lu, Junlin, et al.
Published: (2024)
Demonstration Guided Multi-Objective Reinforcement Learning
by: Lu, Junlin, et al.
Published: (2024)
by: Lu, Junlin, et al.
Published: (2024)
D-SPEAR: Dual-Stream Prioritized Experience Adaptive Replay for Stable Reinforcement Learning in Robotic Manipulation
by: Zhang, Yu, et al.
Published: (2026)
by: Zhang, Yu, et al.
Published: (2026)
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
by: Lu, Junlin, et al.
Published: (2024)
by: Lu, Junlin, et al.
Published: (2024)
Hindsight Preference Replay Improves Preference-Conditioned Multi-Objective Reinforcement Learning
by: Shianifar, Jonaid, et al.
Published: (2026)
by: Shianifar, Jonaid, et al.
Published: (2026)
QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning
by: Stigenberg, Jakob, et al.
Published: (2021)
by: Stigenberg, Jakob, et al.
Published: (2021)
Automated Deep Learning for Load Forecasting
by: Keisler, Julie, et al.
Published: (2024)
by: Keisler, Julie, et al.
Published: (2024)
DiffLoad: Uncertainty Quantification in Electrical Load Forecasting with the Diffusion Model
by: Wang, Zhixian, et al.
Published: (2023)
by: Wang, Zhixian, et al.
Published: (2023)
Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
by: Kainuma, Haruki, et al.
Published: (2025)
by: Kainuma, Haruki, et al.
Published: (2025)
Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data
by: Jayakumar, Surya, et al.
Published: (2026)
by: Jayakumar, Surya, et al.
Published: (2026)
ProActor: Timing-Aware Reinforcement Learning for Proactive Task Scheduling Agents
by: Ding, Lei, et al.
Published: (2026)
by: Ding, Lei, et al.
Published: (2026)
On the Role of DAG topology in Energy-Aware Cloud Scheduling : A GNN-Based Deep Reinforcement Learning Approach
by: Hattay, Anas, et al.
Published: (2026)
by: Hattay, Anas, et al.
Published: (2026)
Policy-Based Deep Reinforcement Learning Hyperheuristics for Job-Shop Scheduling Problems
by: Lassoued, Sofiene, et al.
Published: (2026)
by: Lassoued, Sofiene, et al.
Published: (2026)
Forecasting Anonymized Electricity Load Profiles
by: Fernandez, Joaquin Delgado, et al.
Published: (2025)
by: Fernandez, Joaquin Delgado, et al.
Published: (2025)
Dependency-Aware CAV Task Scheduling via Diffusion-Based Reinforcement Learning
by: Cheng, Xiang, et al.
Published: (2024)
by: Cheng, Xiang, et al.
Published: (2024)
From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
by: Veluru, Suhasnadh Reddy, et al.
Published: (2026)
by: Veluru, Suhasnadh Reddy, et al.
Published: (2026)
Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling
by: Zhang, Cong, et al.
Published: (2022)
by: Zhang, Cong, et al.
Published: (2022)
Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
by: Wang, Tongxi, et al.
Published: (2026)
by: Wang, Tongxi, et al.
Published: (2026)
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
by: Li, Boyang, et al.
Published: (2024)
by: Li, Boyang, et al.
Published: (2024)
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning
by: Shen, Ya, et al.
Published: (2024)
by: Shen, Ya, et al.
Published: (2024)
Topology-Aware and Highly Generalizable Deep Reinforcement Learning for Efficient Retrieval in Multi-Deep Storage Systems
by: Li, Funing, et al.
Published: (2025)
by: Li, Funing, et al.
Published: (2025)
Sample-Efficient Neurosymbolic Deep Reinforcement Learning
by: Veronese, Celeste, et al.
Published: (2026)
by: Veronese, Celeste, et al.
Published: (2026)
Deep Reinforcement Learning for Flexible Job Shop Scheduling with Random Job Arrivals
by: Tang, Yu, et al.
Published: (2026)
by: Tang, Yu, et al.
Published: (2026)
Load and Renewable Energy Forecasting Using Deep Learning for Grid Stability
by: Sarkar, Kamal
Published: (2025)
by: Sarkar, Kamal
Published: (2025)
schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
by: de Puiseau, Constantin Waubert, et al.
Published: (2023)
by: de Puiseau, Constantin Waubert, et al.
Published: (2023)
HGT-Scheduler: Deep Reinforcement Learning for the Job Shop Scheduling Problem via Heterogeneous Graph Transformers
by: Soykan, Bulent
Published: (2026)
by: Soykan, Bulent
Published: (2026)
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control
by: Pang, Gaoyang, et al.
Published: (2021)
by: Pang, Gaoyang, et al.
Published: (2021)
Lo-MARVE: A Low Cost Autonomous Underwater Vehicle for Marine Exploration
by: Mason, Karl, et al.
Published: (2024)
by: Mason, Karl, et al.
Published: (2024)
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
by: Dou, Jizhe, et al.
Published: (2024)
by: Dou, Jizhe, et al.
Published: (2024)
Graph-Enhanced Deep Reinforcement Learning for Multi-Objective Unrelated Parallel Machine Scheduling
by: Soykan, Bulent, et al.
Published: (2026)
by: Soykan, Bulent, et al.
Published: (2026)
To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
by: Boscaro, Maddalena, et al.
Published: (2024)
by: Boscaro, Maddalena, et al.
Published: (2024)
Re-Identifying Kākā with AI-Automated Video Key Frame Extraction
by: Maddigan, Paula, et al.
Published: (2025)
by: Maddigan, Paula, et al.
Published: (2025)
Similar Items
-
A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization
by: Ali, Nawazish, et al.
Published: (2024) -
A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning
by: Ali, Nawazish, et al.
Published: (2024) -
Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms
by: Shah, Mian Ibad Ali, et al.
Published: (2024) -
Peer-to-Peer Energy Trading in Dairy Farms using Multi-Agent Reinforcement Learning
by: Shah, Mian Ibad Ali, et al.
Published: (2025) -
A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming
by: Shah, Mian Ibad Ali, et al.
Published: (2023)