Enterprise AI Analysis
Digital Twins Paradigm: A Systematic Review from the Reinforcement Learning Perspective
Authors: Shahmir Khan Mohammed, Shakti Singh, Rabeb Mizouni, Hadi Otrok, E. Damiani
Published: 03 February 2026 | Online AM: 02 December 2025 | Accepted: 07 November 2025
DOI: 10.1145/3777367 | EISSN: 1557-7341
Executive Impact & Key Metrics
The Digital Twins (DT) paradigm is a powerful tool for simulating and analyzing complex systems, but it faces issues such as limited adaptability, incomplete model representation, suboptimal decision-making, and scalability. Reinforcement Learning (RL) offers a transformative solution, providing unsupervised decision-making and intelligence to address these challenges. This study offers a thorough analysis of RL's application in DT, comparing existing frameworks, assessing advantages and disadvantages, and discussing future research directions, aiming to bridge the gap between digital modeling, simulation, and artificial intelligence.
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Total Citations
173
Total Downloads
2026
Publication Year
5
Key Authors
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Automation in Digital Twins
RL enhances automation in digital twin systems by reducing human intervention and improving capabilities. Key themes include adaptive control, security, and autonomous decision-making. RL algorithms learn from real-time data to continuously improve control processes, adapt security measures to emerging threats, and enable intelligent decision-making, making DT systems more effective in dynamic environments. However, challenges like data requirements and model complexity need careful navigation.
Optimization with RL & Digital Twins
The integration of RL into Digital Twin systems significantly improves resource management efficiency and reduces latency. RL algorithms optimize resource allocation, ensuring efficient utilization and minimizing waste, leading to cost savings and increased sustainability. Furthermore, RL addresses latency reduction by optimizing scheduling, routing, and task allocation, enabling rapid data processing and real-time decision-making for time-sensitive applications. Full potential realization requires addressing model complexity, safety concerns, and exploration/exploitation tradeoffs.
Diagnostics via RL & Digital Twins
RL-driven diagnostics in digital twins are crucial for identifying and resolving problems in physical systems. It enables real-time monitoring, anomaly detection, and predictive maintenance, leading to cost savings and improved performance. Fault detection leverages RL to manage data quality and adjust for residual mistakes, while predictive maintenance forecasts equipment failures and recommends actions. Although powerful, its application is currently limited in manufacturing and big data domains, highlighting a need for broader research into healthcare, transportation, and networking.
61.3%
Projected CAGR for Digital Twin Market by 2028
Enterprise Process Flow
Fine-tuning
→
Decision-making
→
Control
→
Maintenance
→
Safety
| Feature | Traditional DT Systems | RL-Aided Digital Twins |
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Case Study: Adaptive Control in Smart Manufacturing Systems
Problem: Smart Manufacturing Systems (SMSs) require adaptive control to manage system variables and simulate behaviors for optimized production. Traditional methods often struggle with dynamic, changing conditions and require significant human intervention.
Solution: The work in [99] proposes a data-driven approach integrating a Digital Twin with Deep Q-Learning (DQN) for synchronous control. The DT provides virtual event logs (states, actions, rewards) for the RL agent, allowing it to learn optimal control policies in a simulated environment before deployment to the physical plant. This enables continuous adaptation and optimization without constant human input.
Benefit: By integrating DRL-based AI, SMSs can achieve autonomous and adaptive control, enhancing efficiency, reducing downtime, and optimizing production processes. The system can learn and adapt to changing conditions dynamically, leading to superior performance compared to rule-based scheduling, and improving overall system-level digital twin utility by testing intelligent control algorithms in advance.
Calculate Your Potential ROI
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Annual Cost Savings
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Hours Reclaimed Annually
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Your AI-Powered Digital Twin Roadmap
Implementing AI-powered Digital Twins is a strategic journey. Here’s a typical phased approach to transform your operations.
Phase 1: Discovery & Strategy
Assessment of current systems, identification of high-impact use cases, and development of a tailored AI strategy for Digital Twin integration. Define clear objectives and success metrics.
Phase 2: Pilot & Proof-of-Concept
Develop and deploy a pilot Digital Twin with integrated RL in a controlled environment. Validate the model's fidelity and RL agent's performance against key metrics. Gather initial data and feedback.
Phase 3: Scaled Implementation
Expand the Digital Twin solution across more operational areas, integrating with existing enterprise systems. Refine RL models based on broader datasets and real-world feedback. Train personnel.
Phase 4: Continuous Optimization & Innovation
Establish ongoing monitoring, maintenance, and retraining protocols for the Digital Twin and RL agents. Explore advanced features like Meta-Learning, Transfer Learning, and Explainable AI for continuous improvement.
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