Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting

This paper formulates general computation as a feedback-control problem, which allows a robotic agent to autonomously overcome some limitations of standard procedural language programming: resilience to errors and early program termination. The authors demonstrate how this computation becomes a sequential decision making problem, solved with reinforcement learning (RL). They do so 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.


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