Distributional RL yields smoother objectives in chaotic systems because return distributions evolve more regularly than individual trajectories under the 1-Wasserstein metric.
Deep Reinforcement Learning for Active Flow Control Around a Three-dimensional Flow-separated Wing at Re = 1,000.arXiv preprint arXiv:2509.10195,
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Bayesian optimization improved the aerodynamic efficiency of a 30P30N high-lift wing by 10.9% using synthetic jets, while deep reinforcement learning achieved negligible gains due to a penalty-heavy reward function.
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On Distributional Reinforcement Learning in Chaotic Dynamical Systems
Distributional RL yields smoother objectives in chaotic systems because return distributions evolve more regularly than individual trajectories under the 1-Wasserstein metric.
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High-lift Wing Separation Control via Bayesian Optimization and Deep Reinforcement Learning
Bayesian optimization improved the aerodynamic efficiency of a 30P30N high-lift wing by 10.9% using synthetic jets, while deep reinforcement learning achieved negligible gains due to a penalty-heavy reward function.