System and method of identifying self-healing actions for computing systems using reinforcement learning

Abstract

Example implementations relate to method and system for implementation of a software agent in a computing system, to identify the most effective self-healing action to build a Q-table by applying a reinforcement learning technique. In particular, the method includes determining an induced state of the computing system based on an error in one or more components of the computing system and selecting a corrective action corresponding to the induced state based on a plurality of Q-values stored in the Q-table. The method further includes executing the corrective action in the computing system and evaluating one or more parameters of the computing system to determine a current state of the computing system. Further, the method includes updating a Q-value of the plurality of Q-values corresponding to the corrective action, in response to evaluation of the current state of the computing system, to build the Q-table.