Paper Citation Record · LEDGER

Bellman operator convergence enhancements in reinforcement learning algorithms

As of 23 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 2 inbound Pith citation observations for arXiv:2505.14564.

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

pith.paper-citation-record.v1
2505.14564 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:36:23.070508Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T19:15:30.759880Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T19:20:31.176550Z

Reference resolution

22 of 22 outbound references displayed

verified exact3
verified fuzzy13
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 97971769-4e0c-44b1-aa2e-c90c255db885 · outbound

This paper cites Accessed on 13/03/2024.

Bellman operator convergence enhancements in reinforcement learning algorithms Accessed on 13/03/2024

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:26.040351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 88a77697-2960-4bfc-a9da-32b850b2e55e · outbound

This paper cites An alternative softmax operator for reinforcement learning.

Bellman operator convergence enhancements in reinforcement learning algorithms An alternative softmax operator for reinforcement learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:21.276599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22af8a4b-41b4-401c-b211-b638843799b6 · outbound

This paper cites Lipschitz Continuity in Model-based Reinforcement Learning.

Bellman operator convergence enhancements in reinforcement learning algorithms Lipschitz Continuity in Model-based Reinforcement Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:21.402923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30701fa9-700d-44a8-a382-4029391ac28f · outbound

This paper cites Speedy q-learning.

Bellman operator convergence enhancements in reinforcement learning algorithms Speedy q-learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.931030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c70850de-13a6-4feb-9af8-4da18a2eeeb7 · outbound

This paper cites Neuronlike adaptive elements that can solve difficult learning control problems.IEEE transactions on systems, man, and cybernetics, (5):834–846, 1983.

Bellman operator convergence enhancements in reinforcement learning algorithms Neuronlike adaptive elements that can solve difficult learning control problems.IEEE transactions on systems, man, and cybernetics, (5):834–846, 1983

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.847166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 385bb917-34a3-4712-8395-c5d0f5b6d3d3 · outbound

This paper cites Increasing the action gap: New operators for reinforcement learning.

Bellman operator convergence enhancements in reinforcement learning algorithms Increasing the action gap: New operators for reinforcement learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.694636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5dc4b47a-a18f-475e-a079-cbee99a440f0 · outbound

This paper cites Q-learning and enhanced policy iteration in discounted dynamic programming.

Bellman operator convergence enhancements in reinforcement learning algorithms Q-learning and enhanced policy iteration in discounted dynamic programming

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.575426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ee427491-64a4-4073-bbf6-1bbd0e756822 · outbound

This paper cites The Value Function Polytope in Reinforcement Learning.

Bellman operator convergence enhancements in reinforcement learning algorithms The Value Function Polytope in Reinforcement Learning

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:36:23.995209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3396a85f-3cab-412a-ba59-edf573264ac2 · outbound

This paper cites Addison-Wesley Professional, 2019.

Bellman operator convergence enhancements in reinforcement learning algorithms Addison-Wesley Professional, 2019

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.443561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 077553d5-df00-491a-93ba-764231b43acd · outbound

This paper cites Topological Foundations of Reinforcement Learning.

Bellman operator convergence enhancements in reinforcement learning algorithms Topological Foundations of Reinforcement Learning

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:36:23.723956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 94e157b0-8bd1-4747-a679-eb98229684e7 · outbound

This paper cites Reinforcement learning essay (aims-cameroon).

Bellman operator convergence enhancements in reinforcement learning algorithms Reinforcement learning essay (aims-cameroon)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.283575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 12ddeb6d-a788-43bb-b167-c188ddef1d34 · outbound

This paper cites Metrics and continuity in reinforcement learning.

Bellman operator convergence enhancements in reinforcement learning algorithms Metrics and continuity in reinforcement learning

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T15:36:23.456679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 47ff5210-c516-4a95-be5e-bd0fa24ae701 · outbound

This paper cites Markov decision processes and dynamic programming, 2013.

Bellman operator convergence enhancements in reinforcement learning algorithms Markov decision processes and dynamic programming, 2013

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:25.162287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3e58590e-34a4-433b-8663-793648d0ee5d · outbound

This paper cites A General Family of Robust Stochastic Operators for Reinforcement Learning.

Bellman operator convergence enhancements in reinforcement learning algorithms A General Family of Robust Stochastic Operators for Reinforcement Learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:36:23.221020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 83833abb-6141-4a9d-ac0f-03679beb8b2e · outbound

This paper cites Efficient memory-based learning for robot control.

Bellman operator convergence enhancements in reinforcement learning algorithms Efficient memory-based learning for robot control

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:24.998711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a2041a33-4389-44b0-b9f4-c833db4bdcbf · outbound

This paper cites John Wiley & Sons, 2013.

Bellman operator convergence enhancements in reinforcement learning algorithms John Wiley & Sons, 2013

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:24.857823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ef987062-9d92-4e6a-bf8b-2526c97377cb · outbound

This paper cites Efficient Model-free Reinforcement Learning in Metric Spaces.

Bellman operator convergence enhancements in reinforcement learning algorithms Efficient Model-free Reinforcement Learning in Metric Spaces

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:22.717341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 604ee969-6e6f-4f26-b1d7-2859d4bf060b · outbound

This paper cites Generalization in reinforcement learning: Successful examples using sparse coarse coding.Advances in neural information processing systems, 8, 1995.

Bellman operator convergence enhancements in reinforcement learning algorithms Generalization in reinforcement learning: Successful examples using sparse coarse coding.Advances in neural information processing systems, 8, 1995

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:22.778518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5dc5b02-45a1-42f9-9128-f1da5c38c23c · outbound

This paper cites MIT press, 2018.

Bellman operator convergence enhancements in reinforcement learning algorithms MIT press, 2018

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:36:22.839696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4633efd3-8bb1-47ae-95f3-7945135c3b9e · outbound

This paper cites Nova Science Publishers, New York, 2021.

Bellman operator convergence enhancements in reinforcement learning algorithms Nova Science Publishers, New York, 2021

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:24.689116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 024b3833-47fe-415b-b7c1-5109dbabce50 · outbound

This paper cites Github : Basic Reinforcement Learning.

Bellman operator convergence enhancements in reinforcement learning algorithms Github : Basic Reinforcement Learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:24.580346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 31244681-40c7-4d4d-af3f-5c18478d4b53 · outbound

This paper cites Learning from delayed rewards.

Bellman operator convergence enhancements in reinforcement learning algorithms Learning from delayed rewards

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:36:24.241206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 5e064f49-fe9c-44bf-a386-8cd8c3911954 · inbound

Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways cites this paper.

Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways Bellman operator convergence enhancements in reinforcement learning algorithms

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-21T19:20:31.178252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 92b1a724-e897-400f-b5a6-3aec6e20797d · inbound

TabQL: In-Context Q-Learning with Tabular Foundation Models cites this paper.

TabQL: In-Context Q-Learning with Tabular Foundation Models Bellman operator convergence enhancements in reinforcement learning algorithms

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:38:16.878324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.