Efficient Reinforcement Learning for Control

Abstract

The landscape of control systems has evolved rapidly with the emergence of Reinforcement Learning (RL), offering promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, scalability issues, and a lack of structure in learning mech- anisms. This thesis explores a central question: How can we design reinforcement learning algorithms that are not only effective but also computationally effi- cient and scalable for control systems of increasing complexity? To address this, we present a progression of approaches—starting with time-scale decomposition in small- scale systems and moving towards structured and adaptive learning strategies for large-scale, multi-agent control problems. Each chapter builds upon the previous one by introducing new methods tailored to the complexity and scale of the environment, culminating in a unified framework for efficient RL-driven control