Maze-driven self-diagnostics using reinforcement learning

Abstract

Systems and methods are provided for automatedly troubleshooting a computing application (e.g., a cloud-based computing application). An application domain of the computing application is modeled as a two-dimensional array of cells, a first dimension of the array representing components or microservices of the application domain, and a second dimension of the array representing states of the components or microservices, the array including paths between pairs of cells in the array. A troubleshooting goal is defined as a target state of the application domain, the target state corresponding to a target cell in the array. An initial state of the application domain is also provided, the initial state corresponding to an initial cell in the array. A reinforcement-learning-trained machine-learning algorithm can determine a solution path in the array between the initial cell and the target cell. Divergence between a failure case and a solution path indicates a probable failure cause.