mlsanityData to 14 Oct 2026

Reinforcement Learning for Power-Flow Network Analysis

arXiv 2603.05673 ↗First posted 5 Mar 2026cs.LG cs.SC math.AGcited by 0

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

Abstract (arXiv)

The power flow equations are non-linear multivariate equations that describe the relationship between power injections and bus voltages of electric power networks. Given a network topology, we are interested in finding network parameters with many equilibrium points. This corresponds to finding instances of the power flow equations with many real solutions. Current state-of-the art algorithms in computational algebra are not capable of answering this question for networks involving more than a small number of variables. To remedy this, we design a probabilistic reward function that gives a good approximation to this root count, and a state-space that mimics the space of power flow equations. We derive the average root count for a Gaussian model, and use this as a baseline for our RL agents. The agents discover instances of the power flow equations with many more solutions than the average baseline. This demonstrates the potential of RL for power-flow network design and analysis as well as the potential for RL to contribute meaningfully to problems that involve complex non-linear algebra or geometry. footnoteAuthor order alphabetic, all authors contributed equally.

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 7 of its 52 references that are in the map. First posted on arXiv 5 Mar 2026.

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7 of 52 references.

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Same direction, newest first

  1. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systems29 Jun

Other references · 6

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2015Recent Advances in Computational Methods for the Power Flow Equationscited by 2
  3. 2020The Distribution of the Number of Real Solutions to the Power Flow Equationscited by 2
  4. 2020Tangent Quadrics in Real 3-Spacecited by 1
  5. 2020Nodes on quintic spectrahedracited by 1
  6. 2020Exploiting Symmetry in the Power Flow Equations Using Monodromycited by 1

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  1. 2026Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
  2. 2025Optimizing Power Grid Topologies with Reinforcement Learning: A Survey of Methods and Challenges
  3. 2024Learning the Optimal Power Flow: Environment Design Matters
  4. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Survey
  5. 2025Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids
  6. 2025A General Approach of Automated Environment Design for Learning the Optimal Power Flow
  7. 2024State and Action Factorization in Power Grids
  8. 2025RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations