Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:32:09.371004Z
Paper Citation Record · LEDGER
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
As of 15 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2507.02639.
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 2026-08-15T06:32:42.880941+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
29 of 29 outbound references displayed
- verified exact0
- verified fuzzy16
- unresolved13
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 3769e907-d752-4057-a7e3-992114634450 · outbound
This paper cites Then, the function to predict the next state,fs :H→S or fs :H×A→S , can be assigned any GP prior (e.g., SVGP).
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Then, the function to predict the next state,fs :H→S or fs :H×A→S , can be assigned any GP prior (e.g., SVGP)
Reference 1
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation ea5136d9-a939-444f-8742-9480df7738bb · outbound
This paper cites Continuous control with deep reinforcement learning.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Continuous control with deep reinforcement learning
Reference 5
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8c73c0fb-4e37-4650-b5c7-1e43789cb9a0 · outbound
This paper cites Description of each model is enclosed in the figure’s caption.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Description of each model is enclosed in the figure’s caption
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:10.064260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7a4a3c3c-77da-408b-8e07-ba65e2c054b7 · outbound
This paper cites temperature.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning temperature
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:09.885251Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a4f55b76-6244-42b8-91f5-600c23ab8395 · outbound
This paper cites As for what requirements are needed for posterior consistency, we need intuitively that priorπ(θ) do not excludeθ0 from its support.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning As for what requirements are needed for posterior consistency, we need intuitively that priorπ(θ) do not excludeθ0 from its support
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:12.421059Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation fcd90b0e-73c5-47e5-b5d2-a9626ac9857c · outbound
This paper cites This is formalized as follows (Schwartz, 1965; Ghosal & Van der Vaart, 2017).
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning This is formalized as follows (Schwartz, 1965; Ghosal & Van der Vaart, 2017)
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:12.172735Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 11ed430a-7e41-4a25-b3a7-517a3f9d46bd · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 17
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a7b0dae8-ebe4-4c64-b46f-9566fbb021ce · outbound
This paper cites Notice that with(logn)t = 1, the above is equal to the minimax rate (best rate of estimation) for functions in the classCα(X ) (Yang & Barron, 1999).
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Notice that with(logn)t = 1, the above is equal to the minimax rate (best rate of estimation) for functions in the classCα(X ) (Yang & Barron, 1999)
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:11.721602Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6d0b4549-c527-4a97-9064-eb00c3ed9a7d · outbound
This paper cites DefineHα(X ) as the Sobolev space, then: Theorem A.9(Van Der Vaart & Van Zanten (2011)).
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning DefineHα(X ) as the Sobolev space, then: Theorem A.9(Van Der Vaart & Van Zanten (2011))
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:11.450220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7e2bf8d2-0325-4f62-8837-39c181afd654 · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 21
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 6180afa5-335d-4e38-869d-5825620136b5 · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 379dbd98-baef-4992-9149-6f4343c66bcf · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 28
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation df300faf-7bd5-41f3-9950-8c148a7c2875 · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 29
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation b76c4086-3195-4ae8-aefb-fc24a148e3a5 · outbound
This paper cites be incorporated a priori.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning be incorporated a priori
Reference 100
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:10.768612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 83592411-d56e-44e5-8ea4-3e6113700be5 · outbound
This paper cites As stated in main paper, the issue associated with computing IGθ(st,at,st+1) is that it can be done only in a reactive setting wherest+1 is actually revealed to the agent.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning As stated in main paper, the issue associated with computing IGθ(st,at,st+1) is that it can be done only in a reactive setting wherest+1 is actually revealed to the agent
Reference 1948
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:11.161549Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 09935f56-6ca8-460c-ab7f-1f0d035780f6 · outbound
This paper cites Planning to explore via self-supervised world models.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Planning to explore via self-supervised world models
Reference 1965
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:13.233946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 4f15c855-243a-4823-9bee-d2dc0a859558 · outbound
This paper cites Proximal Policy Optimization Algorithms.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Proximal Policy Optimization Algorithms
Reference 1991
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 78eaa033-1f4a-461e-be1b-d4d2cbff72d7 · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 1999
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a715209b-8199-485b-95b5-e021210a55a4 · outbound
This paper cites A bayesian framework for reinforcement learning.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning A bayesian framework for reinforcement learning
Reference 2008
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:13.054614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 3cf3bb99-05af-4547-8fd3-c1533e203c01 · outbound
This paper cites On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
Reference 2009
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d71a2f10-b44e-43c8-9554-db93a73f34ed · outbound
This paper cites Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Reference 2010
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3b4e5787-5471-46f2-a869-b61ab6c8bfe9 · outbound
This paper cites Bayesian Active Learning for Classification and Preference Learning.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Bayesian Active Learning for Classification and Preference Learning
Reference 2012
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c18ec8ce-613f-45ee-b7af-06ee22a14b17 · outbound
This paper cites A unifying view of sparse approximate gaussian process regression.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning A unifying view of sparse approximate gaussian process regression
Reference 2014
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:13.412169Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation a504bef3-d124-4b8c-a59a-df4f7fcb9888 · outbound
This paper cites On a measure of the information provided by an experiment.The Annals of Mathematical Statistics, 27(4):986–1005,.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning On a measure of the information provided by an experiment.The Annals of Mathematical Statistics, 27(4):986–1005,
Reference 2015
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:13.625468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation bdc0d988-8886-4eea-aff3-36cb20fc6265 · outbound
This paper cites an unresolved cited work.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work
Reference 2016
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T20:32:10.306965Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 975aee35-52d6-464a-b8b6-5b95c548c023 · outbound
This paper cites Posterior consistency of dirichlet mixtures in density estimation.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Posterior consistency of dirichlet mixtures in density estimation
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:13.938358Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 55cce8e3-083b-4e2e-a4c2-9853649e1701 · outbound
This paper cites Intrinsic motivation and reinforcement learning.Intrinsically motivated learning in natural and artificial systems, pp.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Intrinsic motivation and reinforcement learning.Intrinsically motivated learning in natural and artificial systems, pp
Reference 2018
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:14.225076Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 5bce1c38-d3dc-4f60-aa41-9136ac3bab57 · outbound
This paper cites Stochastic variational deep kernel learning.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Stochastic variational deep kernel learning
Reference 2020
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T20:32:12.825369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 41516c33-e533-4df7-804d-da608299af87 · outbound
This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:32:07.655869Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.