Paper Citation Record · LEDGER

Robust Markov Decision Processes without Model Estimation

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2302.01248.

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

pith.paper-citation-record.v1
2302.01248 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:15.679390Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T22:03:30.887269Z

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 904566eb-d44f-49e5-89f9-d9408f108001 · inbound

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form cites this paper.

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form Robust Markov Decision Processes without Model Estimation

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:03:30.890063Z

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 c9984c1e-5b5d-49c7-b0a9-41bf196fa032 · inbound

TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning cites this paper.

TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial Planning Robust Markov Decision Processes without Model Estimation

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-11T23:23:34.942702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fc7b9a9-a76c-4ba5-823f-38be90703761 · inbound

Minimax-Optimal Multi-Agent Robust Reinforcement Learning cites this paper.

Minimax-Optimal Multi-Agent Robust Reinforcement Learning Robust Markov Decision Processes without Model Estimation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T00:10:22.378171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da2c0c54-f833-4624-8f60-e7955f2a5dc8 · inbound

Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning cites this paper.

Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning Robust Markov Decision Processes without Model Estimation

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-07T14:37:30.417170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f72f8722-42ed-42c5-8e5f-083a0df66e38 · inbound

Revisiting Subgradient Dominance in Robust MDPs: Counterexamples, Hardness, and Sufficient Conditions cites this paper.

Revisiting Subgradient Dominance in Robust MDPs: Counterexamples, Hardness, and Sufficient Conditions Robust Markov Decision Processes without Model Estimation

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:21:05.334155Z

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 422d1983-51ba-46d4-9354-c117d8998820 · inbound

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions cites this paper.

Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions Robust Markov Decision Processes without Model Estimation

Reference 114

Resolution: unresolved; no resolver link, observed 2026-08-15T14:39:15.679390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.