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Paper Citation Record · LEDGER

Hybrid Reward Architecture for Reinforcement Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1706.04208.

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

pith.paper-citation-record.v1
1706.04208 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:20:01.490092Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T17:58:37.200716Z

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 55632a50-b590-42cd-b58f-ec5c5c28b98a · inbound

Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents cites this paper.

Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents Hybrid Reward Architecture for Reinforcement Learning

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-10T21:20:01.490092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:20:01.490092Z digest=sha256:4aa50aace7405c04daa19e3f75268f951be3862b63f444e6d280b48ae988e460

Observation e0f3a676-79c6-4e0a-9eeb-cb958a3571fe · inbound

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning cites this paper.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Hybrid Reward Architecture for Reinforcement Learning

Reference 130

Resolution
verified exact
local_arxiv, observed 2026-08-09T17:58:37.207275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-09T17:58:36.253277Z digest=sha256:f3ba137a22ba0d26e0b8797918cdbf9af7543a88d26a52a8883329ad579e63b3