REVIEW 1 cited by
Connections Between Adaptive Control and Optimization in Machine Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
This paper demonstrates many immediate connections between adaptive control and optimization methods commonly employed in machine learning. Starting from common output error formulations, similarities in update law modifications are examined. Concepts in stability, performance, and learning, common to both fields are then discussed. Building on the similarities in update laws and common concepts, new intersections and opportunities for improved algorithm analysis are provided. In particular, a specific problem related to higher order learning is solved through insights obtained from these intersections.
Forward citations
Cited by 1 Pith paper
-
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design
A DDPG agent learns the gains of a fixed three-loop missile autopilot; with a shaped reference command in the reward, it matches or outperforms classical gain scheduling in simulation.
Discussion (0). Continue with ORCID to comment.