Automatically tuning a quality of service setting for a distributed storage system with a deep reinforcement learning agent

Systems and methods are described for using a Deep Reinforcement Learning (DRL) agent to automatically tune Quality of Service (QoS) settings of volumes in a distributed storage system (DSS). According to one embodiment, a DRL agent is trained in a simulated environment to select QoS settings (e.g., a value of one or more of a minimum IOPS parameter, a maximum IOPS parameter, and a burst IOPS parameter). The training may involve placing the DRL agent into every feasible state representing combinations of QoS settings, workload conditions, and system metrics for a period of time for multiple iterations, and rewarding the DRL agent for selecting QoS settings that minimize an objective function based on a selected measure of system load. The trained DRL agent may then be deployed to one or more DSSs to constantly update QoS settings so as to minimize the selected measure of system load.