Offline Reinforcement Learning for Fluid Controls: Data-based Multi-observational Policy Extraction
arXiv:2606.31025v1 Announce Type: new Abstract: Active flow control is a fundamental application in engineering. Recent advances in deep reinforcement learning have made progress in this field. However, the classical online RL approaches require extensive real-time interactions with the high fidelity environment, while each sensor configuration change necessitates whole policy retraining. All these factors result in prohibitive computational costs for real-world applications. In this work, we propose a novel offline RL framework that addresses both challenges through data-driven policy extract
The increasing maturity of deep reinforcement learning and the demand for more efficient and less resource-intensive AI training methods are driving innovation in offline RL.
This development allows for more practical and cost-effective application of AI in complex engineering tasks, accelerating the deployment of advanced fluid control technologies.
Offline reinforcement learning reduces the need for extensive real-time interaction and retraining, making AI-driven active flow control more accessible and economical for real-world industrial and defence applications.
- · Aerospace engineering
- · Automotive industry
- · Energy sector
- · AI research institutions
- · Developers reliant solely on online RL
- · Simulation-heavy engineering processes
More widespread adoption of AI for complex physical control systems in critical infrastructure and advanced manufacturing.
Reduced operational costs and improved efficiency in industries like aerospace, energy, and climate control where fluid dynamics are crucial.
Enhanced overall system resilience and performance across national infrastructure and defence platforms through optimized control mechanisms.
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Read at arXiv cs.LG