Paper Citation Record · LEDGER

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps

As of 19 August 2026, this Paper Citation Record lists 10 of 10 outbound references and 1 inbound Pith citation observation for arXiv:2507.10843.

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

pith.paper-citation-record.v1
2507.10843 v1

Coverage vector

measured 10 of 10 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:30:45.925848Z

measured 11 of 11 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T17:33:35.857240Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T23:57:29.172749Z

Reference resolution

10 of 10 outbound references displayed

verified exact0
verified fuzzy3
unresolved6
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d43cd52-33f8-414b-a1be-40b6a0f85e9e · outbound

This paper cites an unresolved cited work.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps Unresolved cited work

Reference 2

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T17:30:46.185997Z

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 bb140678-6e76-4754-986e-6a3dee57ca80 · outbound

This paper cites In contrast, this tendency was not as clearly observed in the HalfCheetah environment.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps In contrast, this tendency was not as clearly observed in the HalfCheetah environment

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:46.073232Z

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 54148415-905d-4502-887e-efee2d875da4 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:30:45.281850Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98439070-3932-4e90-86ec-5c49b2fdcbb1 · outbound

This paper cites Reinforcement Learning via Fenchel-Rockafellar Duality.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps Reinforcement Learning via Fenchel-Rockafellar Duality

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T17:30:45.429840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84d87a11-543b-492d-aef9-0ee18877939c · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T17:30:45.523687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff0ed2f6-bbf0-455a-b574-9aa139060114 · outbound

This paper cites Wasserstein-2 generative networks.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps Wasserstein-2 generative networks

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:46.562888Z

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 71468324-58d0-456d-979f-a06f0f009812 · outbound

This paper cites Behavior Regularized Offline Reinforcement Learning.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps Behavior Regularized Offline Reinforcement Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T17:30:45.689569Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c3fafe3-c5c6-4f2e-b1d2-cbf8ddad0e00 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T17:30:44.978852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57c7f0c7-d0dc-4cd0-b240-22ed0c0e1208 · outbound

This paper cites Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T17:30:44.825946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcb3c231-dd35-46a4-8d32-67ca568d2537 · outbound

This paper cites 7 Experimental Details In AdvW and Q-DOT, the actor, critic, discriminator (for AdvW), and ICNN (for Q-DOT) are all two-layer MLPs with ReLU activations and 256 hidden units.

Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps 7 Experimental Details In AdvW and Q-DOT, the actor, critic, discriminator (for AdvW), and ICNN (for Q-DOT) are all two-layer MLPs with ReLU activations and 256 hidden units

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:30:46.407669Z

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.

Pith citing papers

Observation 99fc9320-c981-40d7-8ff0-335790862422 · inbound

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement cites this paper.

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:57:29.174231Z

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.