  System operators are faced with increasingly volatile operating conditions.
In order to manage system reliability in a cost-effective manner, control room
operators are turning to computerised decision support tools based on AI and
machine learning. Specifically, Reinforcement Learning (RL) is a promising
technique to train agents that suggest grid control actions to operators. In
this paper, a simple baseline approach is presented using RL to represent an
artificial control room operator that can operate a IEEE 14-bus test case for a
duration of 1 week. This agent takes topological switching actions to control
power flows on the grid, and is trained on only a single well-chosen scenario.
The behaviour of this agent is tested on different time-series of generation
and demand, demonstrating its ability to operate the grid successfully in 965
out of 1000 scenarios. The type and variability of topologies suggested by the
agent are analysed across the test scenarios, demonstrating efficient and
diverse agent behaviour.
