Implementing reinforcement learning based flash control

Abstract

A method and system are provided for implementing enhanced flash storage control using reinforcement learning to provide enhanced performance metrics. A flash controller, such as a Reinforcement Learning (RL) flash controller, is coupled to a flash storage. The flash controller defines a feature set of flash parameters determined by a predefined one of a plurality of optimization metrics. The optimization metric is adapted dynamically based upon system workload and system state. The flash controller employing the feature set including at least one feature responsive to erase operations; computes a current system state responsive to the employed feature set; selects actions at each time step by sensing the computed current system state for performing an action to maximize a long term reward, and moves to another state in the system while obtaining a short-term reward for the performed action.