Paper Citation Record · LEDGER

RL as Regressor: A Reinforcement Learning Approach for Function Approximation

As of 18 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 0 inbound Pith citation observations for arXiv:2508.00174.

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

pith.paper-citation-record.v1
2508.00174 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:22:39.912008Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

10 of 10 outbound references displayed

verified exact0
verified fuzzy5
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ce3feee-9866-4728-b44a-08c997b7f8df · outbound

This paper cites Kingma and Jimmy Ba.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Kingma and Jimmy Ba

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T10:22:39.831214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca7d71d4-59ed-4238-a674-fee32b7be47e · outbound

This paper cites The Epoch-Greedy algorithm for contextual multi-armed ban- dits.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation The Epoch-Greedy algorithm for contextual multi-armed ban- dits

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:22:40.247156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e016c715-2f26-4389-ac9a-7f4e96c90cb5 · outbound

This paper cites Continuous control with deep reinforcement learning.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Continuous control with deep reinforcement learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T10:22:39.860700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a00fd9c3-4d79-45c2-9f97-483a6b4b28ef · outbound

This paper cites Rusu, Joel Veness, Marc G.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Rusu, Joel Veness, Marc G

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:22:40.207872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7d71f200-2ace-4a3a-8d17-8c6b71374bca · outbound

This paper cites Prioritized Experience Replay.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Prioritized Experience Replay

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T10:22:39.880110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 481af0a1-4102-481d-ace0-423ff444e8cb · outbound

This paper cites Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes, September.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes, September

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:22:40.160553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab7f593d-a19b-4e53-a746-8f6651ea93f5 · outbound

This paper cites Attention is All you Need.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Attention is All you Need

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:22:40.130166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 028d9631-f1df-4e09-81cf-3f4679d1fa88 · outbound

This paper cites Williams.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Williams

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T10:22:40.102563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ed4e51f2-06d5-4898-8a74-0b97c30afdab · outbound

This paper cites Adam: A Method for Stochastic Optimization.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Adam: A Method for Stochastic Optimization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T10:22:39.840704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71c9dd8f-fd74-41b4-beb6-56d24296c8b6 · outbound

This paper cites Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T10:22:39.899194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.