High Performance Computing Reinforcement Learning Framework for Power System Control.

In this paper, the integration of a power system simulator and reinforcement learning (RL) tools and frameworks is presented. A proposed framework is easily applicable and can serve as a framework for further developing, training, and benchmarking RL algorithms on more complex tasks of power system control. The usage of standard RL frameworks enables a broad range of state-of-the-art algorithms to be implemented with high performance, scalability, and substantial code reuse. Also, the proposed framework design is suitable for scaling onto high-performance computing (HPC) clusters which significantly speeds up the computation. The IEEE 14-bus system is selected to show the simulation results of the proposed method.