Summary of Learning to Steer with Brownian Noise, by Stefan Ankirchner et al.
Learning to steer with Brownian noise
by Stefan Ankirchner, Sören Christensen, Jan Kallsen, Philip Le Borne, Stefan Perko
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes algorithms based on moving empirical averages to solve the bounded velocity follower problem in an ergodic setting, where the decision maker must learn system parameters while controlling. The approach integrates statistical methods with stochastic control theory, leading to a logarithmic expected regret rate. The authors conduct rigorous analysis of convergence rates and estimator risks to achieve this result. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a tricky math problem that helps machines learn from experience without knowing all the rules. It’s like trying to drive a car without knowing the road map. The new approach uses moving averages to figure out how to control things while learning about them. The authors did some really hard math to prove it works, and their results show that this way of thinking can be useful in many situations. |
