Paper Citation Record · LEDGER

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management

As of 20 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2508.08132.

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

pith.paper-citation-record.v1
2508.08132 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:41:43.436762Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-10T03:22:41.774450Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T03:26:44.539206Z

Reference resolution

13 of 13 outbound references displayed

verified exact0
verified fuzzy11
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 418d1743-8472-4722-bea1-5e5c8c3a08dd · outbound

This paper cites Heuristic retailer’s day-ahead pricing based on online- learning of prosumer’s optimal energy management model,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Heuristic retailer’s day-ahead pricing based on online- learning of prosumer’s optimal energy management model,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:45.496697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3587de18-ec9c-49f2-912f-4f0d23401bed · outbound

This paper cites Driving towards net zero emissions: The role of natural resources, government debt and political stability,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Driving towards net zero emissions: The role of natural resources, government debt and political stability,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:45.412711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 92c95661-21b7-427c-b6a6-2923b628033a · outbound

This paper cites Strategies for resilience and battery life extension in the face of communication losses for isolated microgrids,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Strategies for resilience and battery life extension in the face of communication losses for isolated microgrids,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:45.270199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 00c079e6-7443-427e-bbba-dc131d9b3f8b · outbound

This paper cites Towards a framework for measurements of power systems resiliency: Comprehensive review and development of graph and vector-based resilience metrics,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Towards a framework for measurements of power systems resiliency: Comprehensive review and development of graph and vector-based resilience metrics,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:45.142853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d2ac92b2-08b5-446e-92e9-c5da1032e01f · outbound

This paper cites Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:44.927009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c831fb3-515d-4c7f-9228-e11002a93cee · outbound

This paper cites Comparative analysis of control strategies for microgrid energy management with a focus on reinforcement learning,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Comparative analysis of control strategies for microgrid energy management with a focus on reinforcement learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:44.667386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b359223a-2bb3-4276-8968-7626c5660a52 · outbound

This paper cites Reinforcement learning techniques in optimizing energy systems,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Reinforcement learning techniques in optimizing energy systems,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:44.418735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 819106db-29fe-4533-999a-748075dc67e0 · outbound

This paper cites A review of trustworthy and explainable artificial intelligence (xai),.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management A review of trustworthy and explainable artificial intelligence (xai),

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:44.252343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6dade47e-6235-4fcf-8848-c1f3a1c62344 · outbound

This paper cites Explainable reinforcement learning (xrl): a systematic literature review and taxonomy,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Explainable reinforcement learning (xrl): a systematic literature review and taxonomy,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:44.050990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f629b3ca-4274-4839-bf4e-d9d5a6986933 · outbound

This paper cites ” why should i trust you?.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management ” why should i trust you?

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:43.857764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 79c2d0d4-0d8a-475d-b7a2-cb79e51504d4 · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management A Unified Approach to Interpreting Model Predictions

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T21:41:43.220637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4ef4cb7-90f4-46af-8273-92278653e26e · outbound

This paper cites Proximal Policy Optimization Algorithms.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Proximal Policy Optimization Algorithms

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T21:41:43.341282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 404e4acf-2c8e-4dfd-8d3e-898935108b96 · outbound

This paper cites Federated reinforce- ment learning for training control policies on multiple iot devices,.

Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management Federated reinforce- ment learning for training control policies on multiple iot devices,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:41:43.670834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 27b8bd4d-7dbc-4742-8ec1-796233f9ec74 · inbound

SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets cites this paper.

SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets Deep Reinforcement Learning with Local Interpretability for Transparent Microgrid Resilience Energy Management

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-10T03:26:44.540729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.