In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. The RL considerations are reviewed in terms of specific electric power system problems, type of control and RL method used. We also provide observations about past considerations based on a comprehensive review of available publications. The review reveals the RL is considered as viable solutions to many decision and control problems across different time scales and electric power system states. Furthermore, we analyse the perspectives of RL approaches in light of the emergence of new-generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems. The perspectives are also analysed in terms of recent breakthroughs in RL algorithms (Safe RL, Deep RL and path integral control for RL) and other, not previously considered, problems for RL considerations (most notably restorative, emergency controls together with so-called system integrity protection schemes, fusion with existing robust controls, and combining preventive and emergency control).
