Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1903.04959.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:13:08.344754Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T17:20:00.511643Z
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 58fbc369-6f81-4cd0-ac1f-861e330543f7 · inbound
Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes cites this paper.
Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:13:08.344754Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 823bb147-bc2f-4d2e-a120-f9c3de876ffe · inbound
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning cites this paper.
TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-03T19:18:54.934679Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation c18b8a8b-9d03-42dd-a27a-f5eaf9b0f691 · inbound
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning cites this paper.
ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Reference 3
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T17:20:00.513287Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.