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About this paper
Juncal Arbelaiz, Anushri Arora, Jonathan W. Pillow
The authors present a Bayesian active-learning method for identifying controlled dynamics, choosing inputs to reduce predictive uncertainty across a target state–input region. The planner applies the first input, updates its model, and replans. In a noisy bistable-system simulation, it learns faster than white noise; pink noise is the strongest open-loop competitor. The result is limited to this numerical case study.
Summary by alphaXiv.
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