  Motivated by recent advancements in Deep Reinforcement Learning (RL), we have
developed an RL agent to manage the operation of storage devices in a household
and is designed to maximize demand-side cost savings. The proposed technique is
data-driven, and the RL agent learns from scratch how to efficiently use the
energy storage device given variable tariff structures. In most of the studies,
the RL agent is considered as a black box, and how the agent has learned is
often ignored. We explain the learning progression of the RL agent, and the
strategies it follows based on the capacity of the storage device.
