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Paper Citation Record · LEDGER

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning

As of 12 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.

pith.paper-citation-record.v1
2507.02639 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:32:09.371004Z

measured 29 of 29 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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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

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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

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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

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This paper cites temperature.

On Efficient Bayesian Exploration in Model-Based Reinforcement Learning temperature

Reference 10

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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

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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

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work

Reference 17

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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

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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

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work

Reference 21

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work

Reference 23

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work

Reference 28

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work

Reference 29

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning be incorporated a priori

Reference 100

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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

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Planning to explore via self-supervised world models

Reference 1965

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 1991

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Unresolved cited work

Reference 1999

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning A bayesian framework for reinforcement learning

Reference 2008

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty

Reference 2009

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Reference 2010

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Bayesian Active Learning for Classification and Preference Learning

Reference 2012

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning A unifying view of sparse approximate gaussian process regression

Reference 2014

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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

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Reference 2016

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Posterior consistency of dirichlet mixtures in density estimation

Reference 2017

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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

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning Stochastic variational deep kernel learning

Reference 2020

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On Efficient Bayesian Exploration in Model-Based Reinforcement Learning D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 2021

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