mlsanityData to 14 Oct 2026

A Deep Reinforcement Learning Method for Multi-objective Transmission Switching

arXiv 2507.11726 ↗First posted 15 Jul 2025eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Transmission switching is a well-established approach primarily applied to minimize operational costs through strategic network reconfiguration. However, exclusive focus on cost reduction can compromise system reliability. While multi-objective transmission switching can balance cost savings with reliability improvements, feasible solutions become exceedingly difficult to obtain as system scale grows, due to the inherent nonlinearity and high computational demands involved. This paper proposes a deep reinforcement learning (DRL) method for multi-objective transmission switching. The method incorporates a dueling-based actor-critic framework to evaluate the relative impact of each line switching decision within the action space, which improves decision quality and enhances both system reliability and cost efficiency. Numerical studies on the IEEE 118-bus system verify the effectiveness and efficiency of the proposed approach compared to two benchmark DRL algorithms.

Key facts. Cited by 1 paper 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 2 of its 14 references that are in the map. First posted on arXiv 15 Jul 2025.

Builds on

2 of 14 references.

References · 2

  1. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Reinforcement Learning versus Optimization for Optimal Transmission Switching: A Comparative Studycited by 0

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