Research map / Bandits and online learning / Online convex optimization and games
Logarithmic Regret for Online Control
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply OGD to strongly convex surrogate costs over disturbance-action policies | Online control of linear dynamical systems · Linear dynamical systems with strongly convex costs | regret: O(\sqrt{T}) -> \tilde{O}\left(\frac{G^2 W^4}{\alpha \sigma^2} \log^7(T)\right) | Helped |
| Use natural-gradient preconditioning instead of ordinary gradient updates | Online control with disturbance-action policies · Linear dynamical systems with strongly convex costs | regret: \tilde{O}\left(\frac{G W^2}{\alpha \mu} \log^7(T)\right) | Mixed |
| Use natural-gradient updates for spherically symmetric quadratic costs | Online control with disturbance-action policies · Spherically symmetric quadratic costs | regret: \tilde{O}\left(\frac{\beta^2 W^2}{\alpha} \log^7(T)\right) | Helped |
Closest papers by meaning
- Logarithmic Regret for Adversarial Online Control (ICML 2020 · similarity 0.92)
- Online Control of Unknown Time-Varying Dynamical Systems (NeurIPS 2021 · similarity 0.91)
- Rate-Optimal Online Convex Optimization in Adaptive Linear Control (NeurIPS 2022 · similarity 0.91)
- Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis (NeurIPS 2019 · similarity 0.91)
- Geometric Exploration for Online Control (NeurIPS 2020 · similarity 0.90)
- Finite Time Logarithmic Regret Bounds for Self-Tuning Regulation (ICML 2024 · similarity 0.88)
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