Paper Citation Record · LEDGER

Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:1909.01150.

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

pith.paper-citation-record.v1
1909.01150 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 2026-08-10T06:31:04.303077+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:54:25.833975Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T12:48:17.537507Z

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 935a9aa8-71f1-4b99-8138-d8b4689fdcc5 · inbound

The Actor-Critic Update Order Matters for PPO in Federated Reinforcement Learning cites this paper.

The Actor-Critic Update Order Matters for PPO in Federated Reinforcement Learning Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:25.833975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6cff440-07e1-4ff3-b0a8-0cab089b8d64 · inbound

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces cites this paper.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 125

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.394738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19e16767-28a5-46df-b75f-58bd86f541c0 · inbound

Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes cites this paper.

Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T16:09:09.416245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5eeadcc8-f19d-42e5-86ba-ef351f030fbb · inbound

Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum cites this paper.

Optimal Sample Complexity for Single Time-Scale Actor-Critic with Momentum Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:17:36.502446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 436449cc-b1fa-404f-a98c-6ce866dc2e4b · inbound

Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization cites this paper.

Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:51:14.853753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5bb3b89b-3080-43ce-a618-9689a22c7443 · inbound

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets cites this paper.

Select-then-differentiate: Solving Bilevel Optimization with Manifold Lower-level Solution Sets Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.880634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fda4f7a5-6c3a-498d-b41e-b4d900c2539a · inbound

Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with $k$-step Policy Gradients cites this paper.

Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with $k$-step Policy Gradients Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:41:44.208343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0b6d6895-f07a-4d40-9de8-935c1ec04bc1 · inbound

Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation cites this paper.

Randomized Advantage Transformation (RAT): Computing Natural Policy Gradients via Direct Backpropagation Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Reference 112

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:48:17.539175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.