Paper Citation Record · LEDGER

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents

As of 14 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2411.11180.

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

pith.paper-citation-record.v1
2411.11180 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:56:21.470013Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:44:36.631711Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

27 of 27 outbound references displayed

verified exact0
verified fuzzy22
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation 5d034281-218e-4f20-8fd2-a8445b0a4c8f · outbound

This paper cites General nonlinear modal representation of large scale power systems,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents General nonlinear modal representation of large scale power systems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.929539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 911d5e59-24e6-4e98-8348-f1e15b66dda7 · outbound

This paper cites Efficient and scalable reinforcement learning for large-scale network control,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Efficient and scalable reinforcement learning for large-scale network control,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.916828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a01b41c7-96d6-428d-8b51-2eb7aeafef92 · outbound

This paper cites Deep reinforcement learning for real-time power grid topology optimization,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Deep reinforcement learning for real-time power grid topology optimization,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.905242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1d20d713-3a0b-4474-9866-9d49df626b76 · outbound

This paper cites Study on the structural complexity of large scale power grids,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Study on the structural complexity of large scale power grids,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.893119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7cb673ba-343c-44b1-a6ab-c512f4a2d609 · outbound

This paper cites A deep reinforcement learning framework for automatic operation control of power system considering extreme weather events,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents A deep reinforcement learning framework for automatic operation control of power system considering extreme weather events,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.880417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cf633952-cd31-4fa4-9a5a-f13d71e9c862 · outbound

This paper cites Smart grid vulnerability and defense analysis under cascading failure attacks,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Smart grid vulnerability and defense analysis under cascading failure attacks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.867361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 879fc3a0-51cb-4990-b319-8f7a69ff20ea · outbound

This paper cites Learning to run a Power Network Challenge: a Retrospective Analysis.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Learning to run a Power Network Challenge: a Retrospective Analysis

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:21.390206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b487e9c-acd8-4219-ad6d-357f2c780420 · outbound

This paper cites Reinforcement learning agents,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Reinforcement learning agents,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.854227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 077729ad-f221-4e0d-80f4-a1eb5c669681 · outbound

This paper cites Intelligent hur- ricane resilience enhancement of power distribution systems via deep reinforcement learning,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Intelligent hur- ricane resilience enhancement of power distribution systems via deep reinforcement learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.842205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 459578ca-d5dd-4c97-9ace-8ff0f64e1107 · outbound

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

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Curriculum based reinforcement learning of grid topology controllers to prevent thermal cascading,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.829396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64310cc6-e338-4eea-aa71-6b8fee87a07f · outbound

This paper cites Curriculum-based reinforcement learning for distribu- tion system critical load restoration,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Curriculum-based reinforcement learning for distribu- tion system critical load restoration,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.816393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8861dc8a-05c4-4942-90e1-a6101e1e8229 · outbound

This paper cites Resilience enhancement of multi- agent reinforcement learning-based demand response against adversarial attacks,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Resilience enhancement of multi- agent reinforcement learning-based demand response against adversarial attacks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.788909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b340b1aa-2041-454a-9316-6aa78c448b3c · outbound

This paper cites PowerGridworld: A framework for multi-agent reinforcement learning in power systems,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents PowerGridworld: A framework for multi-agent reinforcement learning in power systems,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.775605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 01e9ab9b-6774-4d82-a17b-50d061d44912 · outbound

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

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agents,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.762340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dec25a55-f929-40c1-860e-6d3fcde17fc3 · outbound

This paper cites A new framework integrating reinforcement learning, a rule-based expert system, and decision tree analysis to improve building energy flexibility,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents A new framework integrating reinforcement learning, a rule-based expert system, and decision tree analysis to improve building energy flexibility,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.749134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ad6dcd61-ba13-48d1-8e29-c4cd9c7a9be9 · outbound

This paper cites Grid2Op—A testbed platform to model sequential decision making in power systems,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Grid2Op—A testbed platform to model sequential decision making in power systems,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.736900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d2543ad4-67e2-4a23-b5b2-cf6b68520628 · outbound

This paper cites A Markov decision process to enhance power system operation resilience during hurricanes,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents A Markov decision process to enhance power system operation resilience during hurricanes,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.723538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60ee8d32-a66e-4c7d-a1f5-694411a7d90e · outbound

This paper cites Dynamic power management based on continuous-time Markov decision processes,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Dynamic power management based on continuous-time Markov decision processes,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.709863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 33097a2b-fcca-46e8-bc4a-c2dfcab38f0d · outbound

This paper cites Heterogeneous reinforcement learning for defending power grids against attacks,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Heterogeneous reinforcement learning for defending power grids against attacks,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.695607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5da69116-f0ec-40fc-abda-da001f6973d7 · outbound

This paper cites Pandapower—An open-source Python tool for con- venient modeling, analysis, and optimization of electric power systems,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Pandapower—An open-source Python tool for con- venient modeling, analysis, and optimization of electric power systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.683331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bdbde768-d059-4b21-8b45-af64d4bffe84 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Proximal Policy Optimization Algorithms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:21.445206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd624ecd-7d6d-44b8-9033-c05bfa527006 · outbound

This paper cites Trust Region Policy Optimization.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Trust Region Policy Optimization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:21.449908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbbc7ba5-23a8-4110-8add-993e49083fc7 · outbound

This paper cites Stable-Baselines3: Reliable reinforcement learning implementations,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Stable-Baselines3: Reliable reinforcement learning implementations,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.670327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 424182d8-652c-4ae3-a4ca-28886d7207e9 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:21.457890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42c32616-a1ba-4bde-b843-86ac43eb3934 · outbound

This paper cites Topological graph convolutional networks solutions for power distribution grid planning,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Topological graph convolutional networks solutions for power distribution grid planning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.657876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f43420cb-7752-44d1-a46a-5218bb48138f · outbound

This paper cites Proximal policy optimization with graph neural networks for optimal power flow,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Proximal policy optimization with graph neural networks for optimal power flow,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T18:56:21.466403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ed7f0cf-5df3-4b17-b2fd-6c1d6f3c86de · outbound

This paper cites Fast graph representation learning with PyTorch Geometric,.

Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents Fast graph representation learning with PyTorch Geometric,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:56:21.644824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 8de69fbe-f8f1-457b-a3c6-42e66ec9d0f4 · inbound

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy cites this paper.

Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy Robust Defense Against Extreme Grid Events Using Dual-Policy Reinforcement Learning Agents

Reference 2024

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T15:44:37.817072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.