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Paper Citation Record · LEDGER

Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable

As of 13 August 2026, this Paper Citation Record lists 4 of 4 outbound references and 0 inbound Pith citation observations for arXiv:2411.14496.

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

pith.paper-citation-record.v1
2411.14496 v1

Coverage vector

measured 4 of 4 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:59:55.382826Z

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

4 of 4 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 383bf988-f040-4d4e-b0f5-8885b53ca6ed · outbound

This paper cites Introduction to discrete-event simulation and the simpy lan- guage,.

Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable Introduction to discrete-event simulation and the simpy lan- guage,

Reference 92

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:59:55.648530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T15:59:55.356345Z digest=sha256:19c70ed435642bd5cb7ed9d82858a7c61d54bae7b717388fe9529929ac420d0a

Observation f70b47f4-787c-47e3-aec5-5e7f7136a1ad · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable Proximal Policy Optimization Algorithms

Reference 93

Resolution
unresolved
no resolver link, observed 2026-08-12T15:59:55.365171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:59:55.365171Z digest=sha256:86ec260dc6ced579b5427fe57831dee949605b11cc0bf9f0be31ba8d616eb332

Observation 5722285d-887f-47f2-818d-273acf0d4931 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games,.

Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:59:55.621449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T15:59:55.373595Z digest=sha256:fd805080db64d26e9da02975b80096a83a579461f2a7dd78a2b8ab3a703f156e

Observation 4502e6f6-942c-4b4e-bc5a-3ca4ffd9237d · outbound

This paper cites Node placement optimization under q-coverage and q-connectivity constraints in wireless sensor networks,.

Multi-agent reinforcement learning strategy to maximize the lifetime of Wireless Rechargeable Node placement optimization under q-coverage and q-connectivity constraints in wireless sensor networks,

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-12T15:59:55.382826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:59:55.382826Z digest=sha256:ff6a2a240c1376219fa8333aa88bbf68dac38e087321c0dd90fee5faee54b329

Pith citing papers

No inbound Pith citation observations are available.