Cover Image

Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Goldenits, Georg, Mallinger, Kevin, Raubitzek, Sebastian, Neubauer, Thomas
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Small Language Models for Phishing Website Detection: Cost, Performance, and Privacy Trade-Offs
by: Goldenits, Georg, et al.
Published: (2025)

Digital Twins for forecasting and decision optimisation with machine learning: applications in wastewater treatment
by: Colwell, Matthew, et al.
Published: (2024)

Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
by: Khadivi, Maziyar, et al.
Published: (2023)

Causal prompting model-based offline reinforcement learning
by: Yu, Xuehui, et al.
Published: (2024)

Economic span selection of bridge based on deep reinforcement learning
by: Zhang, Leye, et al.
Published: (2024)

Normalization and effective learning rates in reinforcement learning
by: Lyle, Clare, et al.
Published: (2024)

On the consistency of hyper-parameter selection in value-based deep reinforcement learning
by: Obando-Ceron, Johan, et al.
Published: (2024)

Policy-shaped prediction: avoiding distractions in model-based reinforcement learning
by: Hutson, Miles, et al.
Published: (2024)

An advantage based policy transfer algorithm for reinforcement learning with measures of transferability
by: Alam, Md Ferdous, et al.
Published: (2023)

Guided Safe Shooting: model based reinforcement learning with safety constraints
by: Paolo, Giuseppe, et al.
Published: (2022)

Curriculum reinforcement learning with measurable task representation learning
by: Wen, Yongyan, et al.
Published: (2026)

Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning
by: Costa, Miguel, et al.
Published: (2026)

CHARME: A chain-based reinforcement learning approach for the minor embedding problem
by: Ngo, Hoang M., et al.
Published: (2024)

Simulation-based reinforcement learning for real-world autonomous driving
by: Osiński, Błażej, et al.
Published: (2019)

APF+: Boosting adaptive-potential function reinforcement learning methods with a W-shaped network for high-dimensional games
by: Chen, Yifei, et al.
Published: (2025)

Maximum diffusion reinforcement learning
by: Berrueta, Thomas A., et al.
Published: (2023)

Estimating unknown parameters in differential equations with a reinforcement learning based PSO method
by: Sun, Wenkui, et al.
Published: (2024)

In value-based deep reinforcement learning, a pruned network is a good network
by: Obando-Ceron, Johan, et al.
Published: (2024)

Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning
by: Remonda, Adrian, et al.
Published: (2021)

Position: Foundation Models Need Digital Twin Representations
by: Shen, Yiqing, et al.
Published: (2025)

From Transformers to Large Language Models: A systematic review of AI applications in the energy sector towards Agentic Digital Twins
by: Antonesi, Gabriel, et al.
Published: (2025)

Chaos-based reinforcement learning with TD3
by: Matsuki, Toshitaka, et al.
Published: (2024)

Deep reinforcement learning with time-scale invariant memory
by: Kabir, Md Rysul, et al.
Published: (2024)

Offline reinforcement learning for job-shop scheduling problems
by: Echeverria, Imanol, et al.
Published: (2024)

Counterfactual experience augmented off-policy reinforcement learning
by: Lee, Sunbowen, et al.
Published: (2025)

Delayed homomorphic reinforcement learning for environments with delayed feedback
by: Lee, Jongsoo, et al.
Published: (2026)

Bellman operator convergence enhancements in reinforcement learning algorithms
by: Kadurha, David Krame, et al.
Published: (2025)

Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry
by: Mayr, Michael, et al.
Published: (2024)

Industrial Energy Disaggregation with Digital Twin-generated Dataset and Efficient Data Augmentation
by: Internò, Christian, et al.
Published: (2025)

A Review of Digital Twins and their Application in Cybersecurity based on Artificial Intelligence
by: Homaei, MohammadHossein, et al.
Published: (2023)

Dynamic feature selection in medical predictive monitoring by reinforcement learning
by: Chen, Yutong, et al.
Published: (2024)

Leveraging weights signals -- Predicting and improving generalizability in reinforcement learning
by: Moulin, Olivier, et al.
Published: (2025)

Not all tokens are needed(NAT): token efficient reinforcement learning
by: Sang, Hejian, et al.
Published: (2026)

Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning
by: Robertshaw, Harry, et al.
Published: (2024)

Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
by: Dong, Li, et al.
Published: (2024)

Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin
by: Fortuna, Carolina, et al.
Published: (2024)

Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology
by: Sakhinana, Sagar Srinivas, et al.
Published: (2024)

Digital Twin-Driven Communication-Efficient Federated Anomaly Detection for Industrial IoT
by: Belay, Mohammed Ayalew, et al.
Published: (2026)

CAREL: Instruction-guided reinforcement learning with cross-modal auxiliary objectives
by: Saghafian, Armin, et al.
Published: (2024)

An efficient deep reinforcement learning environment for flexible job-shop scheduling
by: Wu, Xinquan, et al.
Published: (2025)