  Recent challenges in operating power networks arise from increasing energy
demands and unpredictable renewable sources like wind and solar. While
reinforcement learning (RL) shows promise in managing these networks, through
topological actions like bus and line switching, efficiently handling large
action spaces as networks grow is crucial. This paper presents a hierarchical
multi-agent reinforcement learning (MARL) framework tailored for these
expansive action spaces, leveraging the power grid's inherent hierarchical
nature. Experimental results indicate the MARL framework's competitive
performance with single-agent RL methods. We also compare different RL
algorithms for lower-level agents alongside different policies for higher-order
agents.
