Introduction
Reinforcement learning (RL) offers a powerful framework for optimizing complex operational workflows in federal technology environments—particularly where agencies must balance competing priorities, adapt to changing conditions, and operate under strict resource and policy constraints. Unlike traditional rule-based systems, RL enables an agent to learn effective decision strategies through
