# Reinforcement Learning versus Optimization for Optimal Transmission Switching: A Comparative Study

arXiv: https://arxiv.org/abs/2607.10948 · HTML: https://mlsanity.com/paper/2607.10948/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## 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, 10 of 10)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Action Set Based Policy Optimization for Safe Power Grid Management](https://mlsanity.com/paper/2106.15200/) (2021)
- [Contingency-constrained economic dispatch with safe reinforcement learning](https://mlsanity.com/paper/2205.06212/) (2022)
- [Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimization](https://mlsanity.com/paper/2508.01951/) (2025)
- [A Bound Strengthening Method for Optimal Transmission Switching in Power Systems](https://mlsanity.com/paper/1711.10428/) (2017)
- [Optimal Transmission Switching: Improving Solver Performance Using Heuristics](https://mlsanity.com/paper/2106.12331/) (2021)
- [Optimal Transmission Switching with Uncertainties from both Renewable Energy and N-k Contingencies](https://mlsanity.com/paper/2304.13944/) (2023)
- [AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimization](https://mlsanity.com/paper/2411.10528/) (2024)
- [A Deep Reinforcement Learning Method for Multi-objective Transmission Switching](https://mlsanity.com/paper/2507.11726/) (2025)
- [Physics Informed Reinforcement Learning with Gibbs Priors for Topology Control in Power Grids](https://mlsanity.com/paper/2604.01830/) (2026)

## Abstract (arXiv)

Optimal Transmission Switching (OTS) reduces generation cost by strategically opening transmission lines, but its mixed-integer linear program (MILP) formulation scales poorly for large-scale transmission networks. Reinforcement learning (RL) offers a computationally efficient alternative, but existing RL-based OTS approaches rely on soft penalties that permit physical constraint violations. This paper presents a comparison between an RL framework and an MILP-based optimization method for OTS. Case studies were carried out on the IEEE RTS-96 24-bus system; results show that the agent was able to produce near-optimal solutions at low switching budgets and tended to yield suboptimal solutions at high switching budgets. However, the RL agent was able to generate feasible solutions two-to-three orders of magnitude faster than the optimization solver.