Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1708.04133.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T04:39:02.562616Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-03T00:07:28.185044Z
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 b009f6a1-d086-47c7-9cb7-80f05702153e · inbound
DeepMind Control Suite cites this paper.
DeepMind Control Suite Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T07:44:14.763418Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation eaff66d4-1eeb-4170-9459-d4939a30ef50 · inbound
Benchmarking Model-Based Reinforcement Learning cites this paper.
Benchmarking Model-Based Reinforcement Learning Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-25T10:15:37.036256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 62ee2e47-b803-4397-ae12-dfa2aa8d5498 · inbound
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting cites this paper.
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 77
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-17T01:59:51.418148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation decb3e77-a2a3-4f19-9afd-b62762b65cb1 · inbound
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies cites this paper.
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T04:39:02.562616Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93cd6373-7558-49e5-94a6-3954fae50f5f · inbound
Efficient Environment Design for Multi-Robot Navigation via Continuous Control cites this paper.
Efficient Environment Design for Multi-Robot Navigation via Continuous Control Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T19:31:39.916570Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 733260a8-cbdf-487b-b972-06a6fb780a87 · inbound
stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation cites this paper.
stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 14
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T09:01:20.374479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 1a042838-9f5b-4d63-8937-51154da289a4 · inbound
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control cites this paper.
When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 3
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T19:13:52.615078Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 5d37cf92-4ae1-4d0d-9a97-ecef564493a1 · inbound
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning cites this paper.
AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T14:33:30.431054Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 23b79dbb-17ba-4cc3-a50d-944d9465a31f · inbound
On Effectiveness and Efficiency of Agentic Tool-calling and RL Training cites this paper.
On Effectiveness and Efficiency of Agentic Tool-calling and RL Training Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 49
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-29T08:43:15.355895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 8dc3032d-8c6e-4b4d-81bc-197d248b4e4c · inbound
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning cites this paper.
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 74
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T00:07:28.186262Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 956976c8-679c-40ec-99d3-8f12c652ad6c · inbound
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL cites this paper.
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T00:13:47.126677Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.