  The main objective of this research paper is to investigate the local
convergence characteristics of Model-agnostic Meta-learning (MAML) when applied
to linear system quadratic optimal control (LQR). MAML and its variations have
become popular techniques for quickly adapting to new tasks by leveraging
previous learning knowledge in areas like regression, classification, and
reinforcement learning. However, its theoretical guarantees remain unknown due
to non-convexity and its structure, making it even more challenging to ensure
stability in the dynamic system setting. This study focuses on exploring MAML
in the LQR setting, providing its local convergence guarantees while
maintaining the stability of the dynamical system. The paper also presents
simple numerical results to demonstrate the convergence properties of MAML in
LQR tasks.
