Hierarchical Reinforcement Learning for Power Network Topology Control
Blazej Manczak Jan Viebahn Herke van Hoof
Learning in high-dimensional action spaces is a key challenge in applying
reinforcement learning (RL) to real-world systems. In this paper, we study the
possibility of controlling power networks using RL methods. Power networks are
critical infrastructures that are complex to control. In particular, the
combinatorial nature of the action space poses a challenge to both conventional
optimizers and learned controllers. Hierarchical reinforcement learning (HRL)
represents one approach to address this challenge. More precisely, a HRL
framework for power network topology control is proposed. The HRL framework
consists of three levels of action abstraction. At the highest level, there is
the overall long-term task of power network operation, namely, keeping the
power grid state within security constraints at all times, which is decomposed
into two temporally extended actions: 'do nothing' versus 'propose a topology
change'. At the intermediate level, the action space consists of all
controllable substations. Finally, at the lowest level, the action space
consists of all configurations of the chosen substation. By employing this HRL
framework, several hierarchical power network agents are trained for the IEEE
14-bus network. Whereas at the highest level a purely rule-based policy is
still chosen for all agents in this study, at the intermediate level the policy
is trained using different state-of-the-art RL algorithms. At the lowest level,
either an RL algorithm or a greedy algorithm is used. The performance of the
different 3-level agents is compared with standard baseline (RL or greedy)
approaches. A key finding is that the 3-level agent that employs RL both at the
intermediate and the lowest level outperforms all other agents on the most
difficult task. Our code is publicly available.