  Robots and autonomous agents often complete goal-based tasks with limited
resources, relying on imperfect models and sensor measurements. In particular,
reinforcement learning (RL) and feedback control can be used to help a robot
achieve a goal. Taking advantage of this body of work, this paper formulates
general computation as a feedback-control problem, which allows the agent to
autonomously overcome some limitations of standard procedural language
programming: resilience to errors and early program termination. Our
formulation considers computation to be trajectory generation in the program's
variable space. The computing then becomes a sequential decision making
problem, solved with reinforcement learning (RL), and analyzed with Lyapunov
stability theory to assess the agent's resilience and progression to the goal.
We do this through a case study on a quintessential computer science problem,
array sorting. Evaluations show that our RL sorting agent makes steady progress
to an asymptotically stable goal, is resilient to faulty components, and
performs less array manipulations than traditional Quicksort and Bubble sort.
