Feb. 12, 2024, 5:42 a.m. | Mohak Bhardwaj Thomas Lampe Michael Neunert Francesco Romano Abbas Abdolmaleki Arunkumar Byravan Marku

cs.LG updates on arXiv.org arxiv.org

Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale. However, domains such as fluid dynamical systems exhibit complex dynamic phenomena that are hard to simulate at high integration rates, limiting the direct application of modern deep RL algorithms to often expensive or safety critical hardware. In this work, we introduce "Box o Flows", a novel benchtop experimental control system for systematically evaluating RL algorithms in dynamic real-world scenarios. We …

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