Reinforcement Learning for Computing Power Grid Network Operating Functions
High voltage electric power grid physical nature learning is rarely a deterministic process rather it is a stochastic process in nature. Markov decision process and reinforcement learning algorithms are available to learn the quantitative and numerical estimation of the electric power generation, transmission, transformation, and distributing line physical measurement. Power grid managers use reinforcement learning process to regulate or control the parameters within a coded programming and computerized instrumentation. In this paper, we emphasize reinforcement algorithms to simplify medium level power grid problems.