Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Reinforcement Learning for Inventory Networks: Toward Reliable Policy Optimization
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.11246.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T12:12:46.198333Z
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 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+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.