Paper Citation Record · LEDGER

Multi-Objective Reinforcement Learning for Power Grid Topology Control

As of 11 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2502.00040.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.00040 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:53:34.118084Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy23
unresolved6
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84a3ad62-1ccb-41bd-b314-3fff26edd628 · outbound

This paper cites An optimal transmission line switching and bus splitting heuristic incorporating ac and n-1 contingency constraints,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control An optimal transmission line switching and bus splitting heuristic incorporating ac and n-1 contingency constraints,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.557915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e25c7117-659b-407d-ac38-80e6ddeec774 · outbound

This paper cites An iterative approach to grid topology and redispatch optimization in congestion management,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control An iterative approach to grid topology and redispatch optimization in congestion management,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.543821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5848dd4c-d493-44b0-86e0-c881e7a15035 · outbound

This paper cites Learning to run a power network challenge for training topology controllers,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Learning to run a power network challenge for training topology controllers,

Reference 3

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Source-reported events for the cited work

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Observation d461d8ed-e774-4ed0-84d2-f65ef46f055d · outbound

This paper cites Gridoptions tool: Real-world day-ahead congestion management using topological remedial actions,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Gridoptions tool: Real-world day-ahead congestion management using topological remedial actions,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.518096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4fb228b4-1991-45fe-bd65-7b9e6bdfa768 · outbound

This paper cites Transmission congestion management via node-breaker topology con- trol,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Transmission congestion management via node-breaker topology con- trol,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.502785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fbbfa35f-b02c-404d-bd17-31fb4fe69751 · outbound

This paper cites Expert system for topological remedial action discovery in smart grids,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Expert system for topological remedial action discovery in smart grids,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.486858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 45ac5d74-42db-4e3a-a010-3d9c05bda37c · outbound

This paper cites Substation reconfiguration selection algorithm based on ptdfs for congestion management and rl approach,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Substation reconfiguration selection algorithm based on ptdfs for congestion management and rl approach,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.472988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad8a612d-68e1-47f2-943d-0b1df2931dd9 · outbound

This paper cites Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.456155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d8146746-3090-40b0-9f06-342aa134a052 · outbound

This paper cites Reinforcement Learning for Electricity Network Operation.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Reinforcement Learning for Electricity Network Operation

Reference 9

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Source-reported events for the cited work

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Observation 4f254533-7a3d-42fe-bd03-4447ea79e9fa · outbound

This paper cites Learning to run a power network challenge: a retrospective analysis,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Learning to run a power network challenge: a retrospective analysis,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.441756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5d7cc6db-2acc-492c-bcec-8819aeb69371 · outbound

This paper cites Learning to run a power network with trust,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Learning to run a power network with trust,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.397170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 782c4edd-383d-46ba-93d1-5753af442e7d · outbound

This paper cites Ai-based autonomous line flow control via topology adjustment for maximizing time-series atcs,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Ai-based autonomous line flow control via topology adjustment for maximizing time-series atcs,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.258638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9242b1b8-7480-4248-b716-18202c6a82b4 · outbound

This paper cites Winning the l2rpn challenge: Power grid management via semi-markov afterstate actor- critic,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Winning the l2rpn challenge: Power grid management via semi-markov afterstate actor- critic,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.149709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dc82322a-8cb9-4061-82b2-8f00c0bb1329 · outbound

This paper cites Exploring grid topology reconfiguration using a simple deep reinforce- ment learning approach,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Exploring grid topology reconfiguration using a simple deep reinforce- ment learning approach,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:35.037571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 93284dac-bc70-4e45-a775-2216fc2bae8a · outbound

This paper cites Powrl: A reinforcement learning framework for robust management of power networks,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Powrl: A reinforcement learning framework for robust management of power networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.972759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85fa4237-b0bb-41fc-bf7c-df3348101712 · outbound

This paper cites Power Grid Congestion Management via Topology Optimization with AlphaZero.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Power Grid Congestion Management via Topology Optimization with AlphaZero

Reference 16

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Source-reported events for the cited work

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Observation d2c6f519-66d9-488e-a420-a546ede0e34b · outbound

This paper cites Curriculum based reinforcement learning of grid topology controllers to prevent thermal cascading,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Curriculum based reinforcement learning of grid topology controllers to prevent thermal cascading,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.957850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e71c0cc-e032-4190-ad55-ed0a4935b01b · outbound

This paper cites A hybrid curriculum learning and tree search approach for network topology control,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control A hybrid curriculum learning and tree search approach for network topology control,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.943330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02560c27-8614-4b52-93a5-68c55871f904 · outbound

This paper cites Hierarchical reinforcement learning for power network topology control.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Hierarchical reinforcement learning for power network topology control

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.926350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c561920-a8e8-4341-946d-d3ee05265d9f · outbound

This paper cites Multi-agent reinforcement learning for power grid topology optimization.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Multi-agent reinforcement learning for power grid topology optimization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.877748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 64a44615-a5ef-4750-819b-d3e941100de1 · outbound

This paper cites Reward design for intelligent deep reinforce- ment learning based power flow control using topology optimization,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Reward design for intelligent deep reinforce- ment learning based power flow control using topology optimization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.837565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8c3e0d5-0fdd-43dd-b769-9a1d5bf15db7 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Proximal Policy Optimization Algorithms

Reference 22

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Source-reported events for the cited work

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Observation f8b691e3-393e-469e-90bd-68f2e9938c4c · outbound

This paper cites A toolkit for reliable benchmarking and research in multi-objective reinforcement learning,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control A toolkit for reliable benchmarking and research in multi-objective reinforcement learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.721398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d3b76642-729f-4562-81eb-d37efa0b3a61 · outbound

This paper cites Multi-Objective Deep Reinforcement Learning.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Multi-Objective Deep Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T13:53:34.095082Z

Source-reported events for the cited work

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Observation cf3c8c74-4a5e-401e-bd2a-7ddfdf1127c5 · outbound

This paper cites A practical guide to multi- objective reinforcement learning and planning,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control A practical guide to multi- objective reinforcement learning and planning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.614819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d5fe20b2-b884-49fc-bec0-6a2bbfa4e294 · outbound

This paper cites Grid2op,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Grid2op,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.599405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9667b8b-37cf-472f-b715-37119939d02f · outbound

This paper cites Multi-objective decision-theoretic planning,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Multi-objective decision-theoretic planning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.477076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34ac212e-7838-4d01-9aac-32f1501dd167 · outbound

This paper cites DelftBlue Supercomputer (Phase 2),.

Multi-Objective Reinforcement Learning for Power Grid Topology Control DelftBlue Supercomputer (Phase 2),

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T13:53:34.113367Z

Source-reported events for the cited work

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Observation 65bba634-3d78-462f-b494-a1f58703458a · outbound

This paper cites Implementation of multi-objective reinforcement learning for power grid topology control,.

Multi-Objective Reinforcement Learning for Power Grid Topology Control Implementation of multi-objective reinforcement learning for power grid topology control,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:53:34.254296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.