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Paper Citation Record · LEDGER

Implementing the Deep Q-Network

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1711.07478.

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

pith.paper-citation-record.v1
1711.07478 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:45:47.129665Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T06:16:28.064256Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 05de83db-2d0a-45ff-983e-e167b247e56b · inbound

Optimizing Conversational Product Recommendation via Reinforcement Learning cites this paper.

Optimizing Conversational Product Recommendation via Reinforcement Learning Implementing the Deep Q-Network

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T21:45:47.129665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:45:47.129665Z digest=sha256:0b5df743bcd5ae2bc2d48d53c1875547b5ea5bf57965b7a30a65a3f6188cd722

Observation 938d2c47-21cc-4864-a734-f4adea139a82 · inbound

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions cites this paper.

Multi-Agent Reinforcement Learning for Dynamic Pricing in Supply Chains: Benchmarking Strategic Agent Behaviours under Realistically Simulated Market Conditions Implementing the Deep Q-Network

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:29:17.885898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T20:29:14.864682Z digest=sha256:85a1966943a6f2c888dbf620eaf711e7ab02823a2ccaa405839d7d04b1407fad