Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An empirical investigation of the challenges of real-world reinforcement learning
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2003.11881.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T21:36:33.713769Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T14:58:33.280851Z
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 bfc646a4-47b4-4068-84fa-e9b213030d6c · inbound
D4RL: Datasets for Deep Data-Driven Reinforcement Learning cites this paper.
D4RL: Datasets for Deep Data-Driven Reinforcement Learning An empirical investigation of the challenges of real-world reinforcement learning
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T23:19:17.403954Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 7d1a2b99-8cd9-4828-b323-d0a90abe9ea0 · inbound
A Survey of Reinforcement Learning for Optimization in Automation cites this paper.
A Survey of Reinforcement Learning for Optimization in Automation An empirical investigation of the challenges of real-world reinforcement learning
Reference 101
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T21:36:33.713769Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 66dddf44-2a3f-4fb5-9516-549f7878187e · inbound
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning cites this paper.
Gym4ReaL: A Suite for Benchmarking Real-World Reinforcement Learning An empirical investigation of the challenges of real-world reinforcement learning
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:26:50.274555Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f5c35dc2-5b39-452b-ae6e-a70a294d62da · inbound
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation cites this paper.
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation An empirical investigation of the challenges of real-world reinforcement learning
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:25.012176Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 46452406-472d-4d7e-a539-14b491cf4254 · inbound
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation cites this paper.
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation An empirical investigation of the challenges of real-world reinforcement learning
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T18:52:37.718635Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b8dd4c74-3bd0-4599-b14d-7cbbb72012e2 · inbound
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning cites this paper.
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning An empirical investigation of the challenges of real-world reinforcement learning
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T14:33:30.451766Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bb01d20c-aa30-4fcc-99b7-a359deafeaef · inbound
Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer cites this paper.
Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer An empirical investigation of the challenges of real-world reinforcement learning
Reference 5
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T14:58:33.282541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation fbcc7291-354b-44f8-b954-992f5b24ea76 · inbound
HypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems cites this paper.
HypEMBER: Hypernetwork-based Ensemble for Robust Policy Learning of Parametrized Dynamical Systems An empirical investigation of the challenges of real-world reinforcement learning
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T12:16:10.849144Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.