Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2109.06668.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:53:28.864222Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T23:38:00.770777Z
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 e0a9236c-cec0-4a37-8ab9-c6fac411bb07 · inbound
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation cites this paper.
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Reference 66
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T23:38:00.774525Z
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 a016837c-4a05-42e1-b30e-e793c6fd24c5 · inbound
Improving RL Exploration for LLM Reasoning through Retrospective Replay cites this paper.
Improving RL Exploration for LLM Reasoning through Retrospective Replay Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:53:28.864222Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d61e6545-3261-4fdf-a095-c774c0f0ad0a · inbound
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms cites this paper.
Adversarial Imitation Learning with General Function Approximation: Theoretical Analysis and Practical Algorithms Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T10:41:02.779291Z
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.