Personalized Policy Gradient for Uncertain Linear Systems

Core Concepts

Efficiently adapt policies for uncertain linear systems using personalized Moreau envelopes.

Abstract

Introduction to the problem of policy estimation for Linear Quadratic Regulator (LQR) in uncertain systems. Proposal of a Moreau Envelope-based surrogate LQR cost for meta-policy adaptation. Comparison of the proposed approach with Model-Agnostic Meta-Learning (MAML) methods. Detailed algorithm design and convergence analysis for the Moreau Envelope-based Meta Linear Quadratic Regulator (MEMLQR). Numerical experiments showcasing the performance and adaptation capabilities of the MEMLQR algorithm. Empirical comparison with MAML-based approaches for policy optimization in uncertain systems. Conclusion highlighting the benefits of personalized policy gradient methods for uncertain linear systems.

Stats

Numerical results show proposed approach outperforms naive averaging of controllers. Proposed method has better sample complexity than MAML approaches.

Quotes

"We propose a Moreau Envelope-based surrogate LQR cost for meta-policy adaptation." "ME provides better empirical performance than MAML in multi-task setups."

Key Insights Distilled From

A Moreau Envelope Approach for LQR Meta-Policy Estimation

by Ashw... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17364.pdf

Deeper Inquiries

How can personalized policy gradient methods be extended to more complex control systems

Personalized policy gradient methods can be extended to more complex control systems by incorporating additional layers of personalization. This can involve adapting the policy gradient algorithms to handle higher-dimensional state and action spaces, as well as more intricate dynamics in the system. Techniques such as hierarchical reinforcement learning, where policies are learned at multiple levels of abstraction, can be utilized to handle the complexity of the control system. Additionally, incorporating advanced optimization methods, such as meta-learning for adapting the learning rate or exploration strategies, can further enhance the personalization of policy gradients in complex systems.

What are the limitations of using Moreau envelopes for policy adaptation in uncertain systems

While Moreau envelopes offer a powerful framework for personalizing policy adaptation in uncertain systems, they do have limitations. One limitation is the computational complexity involved in solving the inner optimization problem at each iteration, especially in high-dimensional systems. Additionally, the effectiveness of Moreau envelopes may be limited in scenarios where the uncertainties in the system are highly non-linear or non-convex, as the regularization term may not adequately capture the complexity of the system dynamics. Furthermore, the performance of Moreau envelopes can be sensitive to the choice of the regularization parameter, requiring careful tuning for optimal results.

How can the concept of meta-learning be applied to other domains beyond control systems

The concept of meta-learning can be applied to various domains beyond control systems to facilitate rapid adaptation and knowledge transfer. In natural language processing, meta-learning can be used to improve language understanding and generation tasks by leveraging insights from diverse datasets. In computer vision, meta-learning techniques can enhance object recognition and image classification by learning to quickly adapt to new visual environments. In healthcare, meta-learning can aid in personalized treatment recommendations by leveraging past patient data to inform decision-making for new cases. Overall, the principles of meta-learning can be applied across a wide range of domains to enable efficient adaptation and knowledge transfer.