Paper Citation Record · LEDGER

Select before Act: Spatially Decoupled Action Repetition for Continuous Control

As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2502.06919.

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

pith.paper-citation-record.v1
2502.06919 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:59:36.877167Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy6
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f7d6ae5-9ed6-4b8e-a581-a98c210ff778 · outbound

This paper cites an unresolved cited work.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:59:37.333198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5a890792-6fe3-4fed-a26a-b2e5216501ed · outbound

This paper cites Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.808189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa993a52-9e21-4823-a2dc-b4eade1a203e · outbound

This paper cites Between mdps and semi-mdps: A frame- work for temporal abstraction in reinforcement learning.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Between mdps and semi-mdps: A frame- work for temporal abstraction in reinforcement learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:59:37.534495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b169e116-fe5f-4453-a13c-b7b8dd62d751 · outbound

This paper cites Given ∀s ∈ S, a− ∈ A, b∈ {0, 1}|A|, based on the two-stage policy described in Sec.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Given ∀s ∈ S, a− ∈ A, b∈ {0, 1}|A|, based on the two-stage policy described in Sec

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:59:37.434722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bdaa3ce7-b0a6-44d9-af76-95d7d95ff8db · outbound

This paper cites In this work, we utilize SAC implementation and hyper-parameter settings proposed in CleanRL (Huang et al., 2022)1.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control In this work, we utilize SAC implementation and hyper-parameter settings proposed in CleanRL (Huang et al., 2022)1

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:59:37.220408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 759ef4bb-8f6e-4fbb-bbbe-ddaa10a3f0a3 · outbound

This paper cites In addition, there exist two policy networks β and π in the SDAR, both of which need to be optimized through gradient descent.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control In addition, there exist two policy networks β and π in the SDAR, both of which need to be optimized through gradient descent

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:59:37.197036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a8183719-74ef-495c-a8fe-9812ed842bd2 · outbound

This paper cites Mujoco: A physics engine for model-based control.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Mujoco: A physics engine for model-based control

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.843555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7848156d-0c21-4e03-870e-5bc6572499d1 · outbound

This paper cites Alexander Vezhnevets, V olodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Aga- piou, et al.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Alexander Vezhnevets, V olodymyr Mnih, Simon Osindero, Alex Graves, Oriol Vinyals, John Aga- piou, et al

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.850988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f40c908-f94c-402e-a683-fdd955da30ff · outbound

This paper cites Learning options in reinforcement learning.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Learning options in reinforcement learning

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:59:37.559092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8bbb0167-79eb-4ddb-8190-10a6b1ca2ef5 · outbound

This paper cites JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.801576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ff6f801-39b0-41bc-879c-0e2f6e2a47cb · outbound

This paper cites Proximal Policy Optimization Algorithms.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Proximal Policy Optimization Algorithms

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.817060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af141db1-e396-46e5-9003-0ca894e711e6 · outbound

This paper cites Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.770030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfc057f2-c153-4af0-8336-5c33cc73c455 · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Deep reinforcement learning for autonomous driving: A survey

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:59:37.584683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6141761c-52f7-42e1-9cb6-3758d5d04833 · outbound

This paper cites An Analysis of Frame-skipping in Reinforcement Learning.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control An Analysis of Frame-skipping in Reinforcement Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.777054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b916fad1-27fb-49ad-8eb7-0351f3764bfc · outbound

This paper cites Continuous control with deep reinforcement learning.

Select before Act: Spatially Decoupled Action Repetition for Continuous Control Continuous control with deep reinforcement learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T14:59:36.794126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.