Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:58:21.440862Z
Paper Citation Record · LEDGER
Where to Intervene: Action Selection in Deep Reinforcement Learning
As of 14 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2507.04187.
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
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
20 of 20 outbound references displayed
- verified exact4
- verified fuzzy8
- unresolved7
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 9df342b2-8219-4261-af34-b8175019f888 · outbound
This paper cites C.2 Treatment Allocation for Sepsis Patients We utilize the MIMIC-III Clinical Database to construct our environment for Sepsis patients.
Where to Intervene: Action Selection in Deep Reinforcement Learning C.2 Treatment Allocation for Sepsis Patients We utilize the MIMIC-III Clinical Database to construct our environment for Sepsis patients
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:23.609894Z
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 e8bf660f-5299-4e58-8a57-fafc58285a48 · outbound
This paper cites This condition is typically met by standard tabular machine learning algorithms.
Where to Intervene: Action Selection in Deep Reinforcement Learning This condition is typically met by standard tabular machine learning algorithms
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:23.131846Z
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 ec79a4de-c4d4-480d-a0eb-a4465a56a4b2 · outbound
This paper cites Model-Based Reinforcement Learning for Atari.
Where to Intervene: Action Selection in Deep Reinforcement Learning Model-Based Reinforcement Learning for Atari
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:19.733463Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9266fcf-3437-40ca-a0a5-1007293cd133 · outbound
This paper cites Quasi-optimal Reinforcement Learning with Continuous Actions.
Where to Intervene: Action Selection in Deep Reinforcement Learning Quasi-optimal Reinforcement Learning with Continuous Actions
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:58:22.385863Z
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 f7c6235a-514c-4b58-9092-c03351d87ec6 · outbound
This paper cites Sequential Knockoffs for Variable Selection in Reinforcement Learning.
Where to Intervene: Action Selection in Deep Reinforcement Learning Sequential Knockoffs for Variable Selection in Reinforcement Learning
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:58:22.102178Z
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 2b4f1771-5967-4d9a-8cd9-0fd7c74dfb36 · outbound
This paper cites Proximal Policy Optimization Algorithms.
Where to Intervene: Action Selection in Deep Reinforcement Learning Proximal Policy Optimization Algorithms
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:20.695847Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 71d44aa7-5877-4d8c-92cb-277a1e0e7ac9 · outbound
This paper cites FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback.
Where to Intervene: Action Selection in Deep Reinforcement Learning FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:58:21.676126Z
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 afd1b8d0-f784-4674-ad9a-4e02b89a5410 · outbound
This paper cites an unresolved cited work.
Where to Intervene: Action Selection in Deep Reinforcement Learning Unresolved cited work
Reference 18
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T19:58:23.384146Z
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 790f878e-7e0c-4988-9cee-407c0419c501 · outbound
This paper cites Then for suchϵ, denote Ω :={i :ϵi =−1}, which is a subset ofH0 by the assumption (and recall thatH0 is the collection of all null variables).
Where to Intervene: Action Selection in Deep Reinforcement Learning Then for suchϵ, denote Ω :={i :ϵi =−1}, which is a subset ofH0 by the assumption (and recall thatH0 is the collection of all null variables)
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:22.869856Z
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 9378e9b2-1a72-447f-8335-7fa9a1e3eff7 · outbound
This paper cites Generalized Fisher Score for Feature Selection.
Where to Intervene: Action Selection in Deep Reinforcement Learning Generalized Fisher Score for Feature Selection
Reference 2009
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:19.447628Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31e1abca-1c61-4ab8-83b0-a0716e62a01e · outbound
This paper cites Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions.
Where to Intervene: Action Selection in Deep Reinforcement Learning Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Reference 2011
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:20.864765Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b809d0b4-1ece-45db-b4af-c2a9d1f658af · outbound
This paper cites Sample Efficient Feature Selection for Factored MDPs.
Where to Intervene: Action Selection in Deep Reinforcement Learning Sample Efficient Feature Selection for Factored MDPs
Reference 2012
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T19:58:22.570256Z
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 b228eede-2d25-4c27-b197-7cad3bac34da · outbound
This paper cites Modern perspectives on reinforcement learning in finance.Modern Perspectiveson ReinforcementLearning in Finance (September 6, 2019).
Where to Intervene: Action Selection in Deep Reinforcement Learning Modern perspectives on reinforcement learning in finance.Modern Perspectiveson ReinforcementLearning in Finance (September 6, 2019)
Reference 2013
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:24.254348Z
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 16e60d3e-f86f-49f5-b7a5-fbdd0d0b02b8 · outbound
This paper cites Growing action spaces.
Where to Intervene: Action Selection in Deep Reinforcement Learning Growing action spaces
Reference 2018
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:24.587546Z
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 56d90c08-b572-469f-96eb-ba29dc9da6cd · outbound
This paper cites Action space shaping in deep reinforcement learning.
Where to Intervene: Action Selection in Deep Reinforcement Learning Action space shaping in deep reinforcement learning
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:24.408502Z
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 94004926-3825-4132-9d68-ab21500bb345 · outbound
This paper cites Playing Atari with Deep Reinforcement Learning.
Where to Intervene: Action Selection in Deep Reinforcement Learning Playing Atari with Deep Reinforcement Learning
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:20.548361Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 602f891a-aa8c-49bb-874c-e95ad1ecf200 · outbound
This paper cites Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling.
Where to Intervene: Action Selection in Deep Reinforcement Learning Deep reinforcement learning in continuous action spaces: a case study in the game of simulated curling
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:24.078551Z
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 072990f4-7d05-4765-aeb1-0fd3c5c074a4 · outbound
This paper cites Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection.
Where to Intervene: Action Selection in Deep Reinforcement Learning Auto-Encoding Knockoff Generator for FDR Controlled Variable Selection
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:58:20.357047Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation caf74bf3-4425-4c58-8be6-17dff894d6c5 · outbound
This paper cites Gene Hunting with Knockoffs for Hidden Markov Models.
Where to Intervene: Action Selection in Deep Reinforcement Learning Gene Hunting with Knockoffs for Hidden Markov Models
Reference 2023
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T19:58:21.857131Z
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 f5db8414-e75c-4636-956e-1f35771c0f4f · outbound
This paper cites (2023) adopted a two-stage framework, performing variable selection offline before applying reinforcement learning.
Where to Intervene: Action Selection in Deep Reinforcement Learning (2023) adopted a two-stage framework, performing variable selection offline before applying reinforcement learning
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T19:58:23.886205Z
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.
Pith citing papers
No inbound Pith citation observations are available.