What type of study is this?

This is a Quantitative Study study.

October 13, 2025Open Access

Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective

View Full Paper

Ask AI

Bookmark

Authors

YPYunian PanKyoto University TLTao LiBeijing Academy of Agricultural and Forestry Sciences QZQuanyan ZhuNew York University

Discussion

Member takes

Implication

This research integrates zero-order optimization in meta-learning for linear quadratic regulators, suggesting improvements in convergence and stability.

Key Points

The proposed algorithm optimizes in heterogeneous dynamic systems, enhancing efficiency and performance.
Stability and convergence guarantees are established for gradient descent in the meta-objective function.
The use of zeroth-order estimation simplifies the learning process without needing Hessian information.
Sample complexity conditions are outlined to support the theoretical claims made in this work.

Cite This Study

Pan et al. (2025) studied this question.

synapsesocial.com/papers/68ecc715d1cc7436f7d18a0d https://doi.org/10.48550/arxiv.2503.00385

View Full Paper

Ask AI

Bookmark

Implication

This research integrates zero-order optimization in meta-learning for linear quadratic regulators, suggesting improvements in convergence and stability.

Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

Model-Agnostic Meta-Policy Optimization via Zeroth-Order Estimation: A Linear Quadratic Regulator Perspective

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider