|
| | | | |
| | | |
| Stat |
Members: 3665
Articles: 2'599'751
Articles rated: 2609
25 January 2025 |
|
| | | | |
|
Article overview
| |
|
|
Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control | | Hongyu Zhou
; Zirui Xu
; Vasileios Tzoumas
; | | Date: |
2 Jan 2023 | | Abstract: | Projection operations are a typical computation bottleneck in online
learning. In this paper, we enable projection-free online learning within the
framework of Online Convex Optimization with Memory (OCO-M) -- OCO-M captures
how the history of decisions affects the current outcome by allowing the online
learning loss functions to depend on both current and past decisions.
Particularly, we introduce the first projection-free meta-base learning
algorithm with memory that minimizes dynamic regret, i.e., that minimizes the
suboptimality against any sequence of time-varying decisions. We are motivated
by artificial intelligence applications where autonomous agents need to adapt
to time-varying environments in real-time, accounting for how past decisions
affect the present. Examples of such applications are: online control of
dynamical systems; statistical arbitrage; and time series prediction. The
algorithm builds on the Online Frank-Wolfe (OFW) and Hedge algorithms. We
demonstrate how our algorithm can be applied to the online control of linear
time-varying systems in the presence of unpredictable process noise. To this
end, we develop the first controller with memory and bounded dynamic regret
against any optimal time-varying linear feedback control policy. We validate
our algorithm in simulated scenarios of online control of linear time-invariant
systems. | | Source: | arXiv, 2301.00497 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER