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Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advances
Multi-agent reinforcement learning (MARL) has long been a significant an...
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Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration
Exploration efficiency is a challenging problem in multi-agent reinforce...
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UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Recent advances in multi-agent reinforcement learning have been largely ...
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AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning
Real world multi-agent tasks often involve varying types and quantities ...
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Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning
Exploration of the high-dimensional state action space is one of the big...
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Towards Physics-informed Deep Learning for Turbulent Flow Prediction
While deep learning has shown tremendous success in a wide range of doma...
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Automating Turbulence Modeling by Multi-Agent Reinforcement Learning
The modeling of turbulent flows is critical to scientific and engineering problems ranging from aircraft design to weather forecasting and climate prediction. Over the last sixty years numerous turbulence models have been proposed, largely based on physical insight and engineering intuition. Recent advances in machine learning and data science have incited new efforts to complement these approaches. To date, all such efforts have focused on supervised learning which, despite demonstrated promise, encounters difficulties in generalizing beyond the distributions of the training data. In this work we introduce multi-agent reinforcement learning (MARL) as an automated discovery tool of turbulence models. We demonstrate the potential of this approach on Large Eddy Simulations of homogeneous and isotropic turbulence using as reward the recovery of the statistical properties of Direct Numerical Simulations. Here, the closure model is formulated as a control policy enacted by cooperating agents, which detect critical spatio-temporal patterns in the flow field to estimate the unresolved sub-grid scale (SGS) physics. The present results are obtained with state-of-the-art algorithms based on experience replay and compare favorably with established dynamic SGS modeling approaches. Moreover, we show that the present turbulence models generalize across grid sizes and flow conditions as expressed by the Reynolds numbers.
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