# Multi-Objective Reinforcement Learning for Power Grid Topology Control

arXiv: https://arxiv.org/abs/2502.00040 · HTML: https://mlsanity.com/paper/2502.00040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/1610.02707/) (2016)
- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Learning to run a power network challenge for training topology controllers](https://mlsanity.com/paper/1912.04211/) (2019)
- [Reinforcement Learning for Electricity Network Operation](https://mlsanity.com/paper/2003.07339/) (2020)
- [Power Grid Congestion Management via Topology Optimization with AlphaZero](https://mlsanity.com/paper/2211.05612/) (2022)
- [PowRL: A Reinforcement Learning Framework for Robust Management of Power Networks](https://mlsanity.com/paper/2212.02397/) (2022)
- [Learning to run a power network with trust](https://mlsanity.com/paper/2110.12908/) (2021)
- [Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents](https://mlsanity.com/paper/2304.00765/) (2023)
- [AI-Based Autonomous Line Flow Control via Topology Adjustment for Maximizing Time-Series ATCs](https://mlsanity.com/paper/1911.04263/) (2019)
- [Exploring grid topology reconfiguration using a simple deep reinforcement learning approach](https://mlsanity.com/paper/2011.13465/) (2020)
- [Hierarchical Reinforcement Learning for Power Network Topology Control](https://mlsanity.com/paper/2311.02129/) (2023)
- [Curriculum Based Reinforcement Learning of Grid Topology Controllers to Prevent Thermal Cascading](https://mlsanity.com/paper/2112.09996/) (2021)

## Built on by (most cited first, 1 of 1)

- [PRISM: Parallel Reward Integration with Symmetry for MORL](https://mlsanity.com/paper/2602.18277/) (2026)

## Abstract (arXiv)

Transmission grid congestion increases as the electrification of various sectors requires transmitting more power. Topology control, through substation reconfiguration, can reduce congestion but its potential remains under-exploited in operations. A challenge is modeling the topology control problem to align well with the objectives and constraints of operators. Addressing this challenge, this paper investigates the application of multi-objective reinforcement learning (MORL) to integrate multiple conflicting objectives for power grid topology control. We develop a MORL approach using deep optimistic linear support (DOL) and multi-objective proximal policy optimization (MOPPO) to generate a set of Pareto-optimal policies that balance objectives such as minimizing line loading, topological deviation, and switching frequency. Initial case studies show that the MORL approach can provide valuable insights into objective trade-offs and improve Pareto front approximation compared to a random search baseline. The generated multi-objective RL policies are 30% more successful in preventing grid failure under contingencies and 20% more effective when training budget is reduced - compared to the common single objective RL policy.