This paper presents neuro-symbolic computer use, a method for learning reusable policies for reliable and efficient execution of recurring workflows. The approach combines executable code with neural models to delegate observation-dependent decisions, and learns policies through neuro-symbolic policy iteration. The result is a reusable policy that can be applied across different runs and tasks, with significant improvements in efficiency and reliability compared to traditional computer-use agents.