Learning automata constitute a branch of reinforcement learning that enables algorithms to iteratively refine their actions through continual interaction with uncertain, or stochastic, environments.

Through reinforcement learning, the algorithm considers positive and negative outcomes from previous charging sessions, such as meeting desired charge levels or exceeding peak thresholds.