Pith. sign in

Paper Citation Record · LEDGER

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning

As of 16 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:1908.08578.

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

pith.paper-citation-record.v1
1908.08578 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:50:05.260925Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

16 of 16 outbound references displayed

  • verified exact0
  • verified fuzzy14
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 634522b1-5dec-4948-9f05-fa74bcade72f · outbound

This paper cites an unresolved cited work.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Unresolved cited work

Reference 1

Resolution
unresolved
raw_fallback, observed 2026-08-14T11:50:05.570724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.178853Z digest=sha256:c97ae3964d8c06b5e5170174f4c39c5b25e327d9f94d0e732bf674922a9ca57b

Observation 15538df1-2ddf-4fe8-958c-a8267168e2b2 · outbound

This paper cites Analysis of temporal-diffference learning with function approximation,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Analysis of temporal-diffference learning with function approximation,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.554277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.186024Z digest=sha256:2a7df997fcf692411cb8072de1e36dd69e71fd74b1fb616bd03d59b538eb390e

Observation 6a23a1f1-f53d-4acb-a896-19830c96b2ea · outbound

This paper cites Learning to predict by the methods of temporal differences,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Learning to predict by the methods of temporal differences,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.530431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.191383Z digest=sha256:bc9eec48cb5aa4d97da6c4ce8701b1aa31b8a86491ff6082f9ffe2f4f74593c1

Observation c8cd6ad9-9d42-4583-9223-79d4f36780c7 · outbound

This paper cites Least-squares policy iteration,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Least-squares policy iteration,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.511381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.196680Z digest=sha256:2aae14dcf042782491523f54dff385023dbe6bb7b8fbd1298dbf4e75bcc9938a

Observation 29ec5b38-eaf6-41c3-989c-8e9a06fcd187 · outbound

This paper cites Value function approximation in reinforcement learning using the fourier basis,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Value function approximation in reinforcement learning using the fourier basis,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.493290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.201901Z digest=sha256:131dd6feec9709deb64bbb9a0b50e00f5c8b7316432834d4d755712276853078

Observation a51d2b79-16cd-4fa4-9798-9e92111dcfbf · outbound

This paper cites Whiteson, Adaptive Representations for Reinforcement Learning, vol.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Whiteson, Adaptive Representations for Reinforcement Learning, vol

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.474530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.208517Z digest=sha256:26b7d8271d70578edc4288193c74520821b66536b3f8778584ac82a42e8dc54d

Observation 39aae30c-3180-441f-b247-45a591fce848 · outbound

This paper cites Meyer, Wavelets and operators, vol.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Meyer, Wavelets and operators, vol

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.457686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.214309Z digest=sha256:0527b4ae7c2a76d8c3e280fbc0e245a8567b214a1b6defecc2912effe41ed96f

Observation 0d02e190-7f55-4a17-b0c5-230338c80c1c · outbound

This paper cites Tree approximation and optimal encoding,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Tree approximation and optimal encoding,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.441539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.219145Z digest=sha256:00278f128b9c0f06311128627def695b4550df39d03b00be2063e0b0c2c6eb40

Observation 72b2bca3-3a53-4214-9bc5-6a151ea21d90 · outbound

This paper cites Directional compactly supported box spline tight framelets with simple geometric structure,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Directional compactly supported box spline tight framelets with simple geometric structure,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.424527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.224111Z digest=sha256:a4a7674f3490e42666a538b748f4fbd04cdd29ca3f96f4ce13e79fc706da9610

Observation 2f55545d-b241-49c3-8b9c-c70536ae80e0 · outbound

This paper cites The best m-term approximation and greedy algorithms,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning The best m-term approximation and greedy algorithms,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.407351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.229112Z digest=sha256:aba91ba3728500cd1fdb5f27674cbd958732048356918ceef0b390912cae2cb9

Observation 65f79afe-a398-4a26-9a0c-3533cdf6ffd7 · outbound

This paper cites Nonlinear approximation,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Nonlinear approximation,

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-14T11:50:05.234576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:50:05.234576Z digest=sha256:a7c1a2e7ed0ccdadefdf5dc4931dcbef96a6fa0faa43516f21f159b32f2c88ec

Observation c31d48a9-4866-4218-801a-6e2f2433e4ec · outbound

This paper cites Convergence rates of multiscale and wavelet expansions,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Convergence rates of multiscale and wavelet expansions,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.375896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.239851Z digest=sha256:5402d2c10529f85b9a3fb2826f03555c07cf705c7b1927e1e6141740cdb22b93

Observation d1712db2-613f-4528-8772-7dc196b5e426 · outbound

This paper cites Han, Framelets and wavelets: algorithms, analysis, and applications, Birkh ¨auser Basel, 2018.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Han, Framelets and wavelets: algorithms, analysis, and applications, Birkh ¨auser Basel, 2018

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.355244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.244918Z digest=sha256:4e41661ce9af899174545394b1b48af6cd2ecc50a6a400dfdeaf0c43582db2ce

Observation b90fe0fe-7dfe-42bf-9a56-c5f1a71a162f · outbound

This paper cites Neuronlike adaptive elements that can solve difficult learning control problems,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Neuronlike adaptive elements that can solve difficult learning control problems,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.337398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.250899Z digest=sha256:89a3a96fb00de7950924f72c3025e3896be2636e355d6b225b93475f9725ab2f

Observation 24d4747a-6c6c-4822-9c34-ec61a1a8f740 · outbound

This paper cites Generalization in reinforcement learning: Successful examples using sparse coarse coding,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Generalization in reinforcement learning: Successful examples using sparse coarse coding,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.320801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.255965Z digest=sha256:e4a8574096738c270ec543dc693bbef40af8f616e5417d0d2b26e1ddb3fd6e79

Observation cff0c88b-5c92-4c58-baa7-b8e322d8b048 · outbound

This paper cites Variable resolution discretization in optimal control,.

On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning Variable resolution discretization in optimal control,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:50:05.302679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-14T11:50:05.260925Z digest=sha256:aa85f12ef6e3a2db89a117752a9fe0da2eb93a33e48d87a69638e38a42aa19bf

Pith citing papers

No inbound Pith citation observations are available.