DescriptionParameter tuning is an important task of storage performance optimization. Current practice usually involves numerous tweak-benchmark cycles that are slow and costly. We present CAPES, a model-less deep reinforcement learning-based unsupervised parameter tuning system driven by a deep neural network (DNN). It is designed to find optimal values for computer systems that have tunable parameters, from a simple client-server system to a large data center, where human tuning can be costly and often cannot achieve optimal performance. CAPES takes periodic measurements of a target computer system's state, and trains a DNN which uses Q-learning to suggest changes to the system's current parameter values. CAPES is minimally intrusive, and can be deployed into a production system to collect training data and suggest tuning actions during the system's daily operation. Evaluation of a prototype on a Lustre file system demonstrates an increase in I/O throughput up to 45% at saturation point.