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Deep Reinforcement Learning for Active Flow Control

Deep reinforcement learning (DRL) has emerged as a transformative approach in the realm of fluid dynamics, offering a data-driven framework to tackle the intrinsic complexities of active flow control.
International Monetary Fund2y

Deep Reinforcement Learning: Emerging Trends in Macroeconomics and Future Prospects

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A ...
Risk4y

Goal-based wealth management with generative reinforcement learning

Matthew Dixon and Igor Halperin develop a reinforcement learning (RL) approach to goal-based wealth management problems such as optimisation of retirement plans or target date funds. They present ...