Paper Citation Record · LEDGER

Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2306.11246.

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

pith.paper-citation-record.v1
2306.11246 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:53:15.218638Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T21:43:45.376424Z

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 f79a741c-2add-41c0-a135-45c3eb03b0aa · inbound

A Planning Framework for Adaptive Labeling cites this paper.

A Planning Framework for Adaptive Labeling Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T16:56:56.474138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48b52071-a7b4-412d-b30f-1c55b309f4a2 · inbound

A Study of Data-driven Methods for Inventory Optimization cites this paper.

A Study of Data-driven Methods for Inventory Optimization Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T21:53:15.218638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5754a304-ccf5-44a1-8631-7e574f3bcf91 · inbound

Adaptive Resolving Methods for Reinforcement Learning with Function Approximations cites this paper.

Adaptive Resolving Methods for Reinforcement Learning with Function Approximations Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T20:54:54.876561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3df5bba4-b0a5-407b-b6e5-fedcd6ece0b9 · inbound

Structure-Informed Deep Reinforcement Learning for Inventory Management cites this paper.

Structure-Informed Deep Reinforcement Learning for Inventory Management Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 1951

Resolution: unresolved; no resolver link, observed 2026-08-06T12:12:46.198333Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48a6045d-323d-47b2-8d22-23d781bacdc7 · inbound

Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems cites this paper.

Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:05:02.392692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03a5e6fd-1f0e-4e45-be95-25190135fa9b · inbound

Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems cites this paper.

Ready from Day 1: Population-Aware Coordination for Large-Scale Constrained Multi-Agent Systems Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-20T21:43:45.378255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51147552-4ed2-47bc-b612-0044e10b8bf7 · inbound

Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients cites this paper.

Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 119

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T02:13:30.211768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b9ae549-695b-4977-a0cd-6fc75c5940b7 · inbound

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection cites this paper.

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T09:06:00.999288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.