Paper Citation Record · LEDGER

Combining Trained Models in Reinforcement Learning

As of 23 July 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2605.02159.

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

pith.paper-citation-record.v1
2605.02159 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-08T19:24:04.574344Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-23T06:31:01.910684+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

24 of 24 outbound references displayed

verified exact2
verified fuzzy22
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2dcd05b3-45ed-4b75-a274-73ff85304b96 · outbound

This paper cites Human-level control through deep reinforcement learning.

Combining Trained Models in Reinforcement Learning Human-level control through deep reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.917538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 62bb8c33-8fca-4077-94f4-13a9bf917b31 · outbound

This paper cites Mastering the game of Go without human knowledge.

Combining Trained Models in Reinforcement Learning Mastering the game of Go without human knowledge

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.911932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 898c361a-ba48-4833-9624-e925ed2c6aec · outbound

This paper cites Policy Distillation.

Combining Trained Models in Reinforcement Learning Policy Distillation

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-09T05:55:30.231690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 424c9021-af0d-46db-a6cd-6dd297e8f575 · outbound

This paper cites Context-aware policy reuse.

Combining Trained Models in Reinforcement Learning Context-aware policy reuse

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-08T19:29:05.471106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 09aeaef8-53e9-4d76-ab63-f1a9dfa21c9f · outbound

This paper cites Efficient bayesian policy reuse with a scalable observation model in deep reinforcement learning.

Combining Trained Models in Reinforcement Learning Efficient bayesian policy reuse with a scalable observation model in deep reinforcement learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.932636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 57c7b723-d9d4-49b8-844e-418020c9cc92 · outbound

This paper cites Model-based reinforcement learning with probabilistic ensemble terminal critics for data-efficient control applications.

Combining Trained Models in Reinforcement Learning Model-based reinforcement learning with probabilistic ensemble terminal critics for data-efficient control applications

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.892264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 8a5c373b-efd2-48c4-936c-6a3152e16e0e · outbound

This paper cites FedDOVe: A federated deep Q-learning-based offloading for vehicular fog computing.

Combining Trained Models in Reinforcement Learning FedDOVe: A federated deep Q-learning-based offloading for vehicular fog computing

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.944483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 625371cc-99db-460a-a3f8-42e2f6851413 · outbound

This paper cites Federated reinforcement learning framework for mobile robot navigation using ROS and gazebo.

Combining Trained Models in Reinforcement Learning Federated reinforcement learning framework for mobile robot navigation using ROS and gazebo

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.940208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 84ed5440-4191-4c96-b11e-4dda6cb89933 · outbound

This paper cites Transfer learning for reinforcement learning domains: A survey.

Combining Trained Models in Reinforcement Learning Transfer learning for reinforcement learning domains: A survey

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.877506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation f1708d71-4a22-43ac-8126-c7cdcb43a562 · outbound

This paper cites Sim-to-real transfer in deep reinforcement learning for robotics: A survey.

Combining Trained Models in Reinforcement Learning Sim-to-real transfer in deep reinforcement learning for robotics: A survey

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.904614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation b3bfbebe-4bf7-4da9-a8c2-e9169598cc6a · outbound

This paper cites A survey on transfer reinforcement learning.

Combining Trained Models in Reinforcement Learning A survey on transfer reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.888904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation ac9ec023-1889-4481-822b-083db532c5e5 · outbound

This paper cites Importance prioritized policy distillation.

Combining Trained Models in Reinforcement Learning Importance prioritized policy distillation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.959670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 91de22cc-7038-4135-b68b-3535e83e23d1 · outbound

This paper cites Online policy distillation with decision-attention.

Combining Trained Models in Reinforcement Learning Online policy distillation with decision-attention

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.895758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation cc44dd9b-da6e-43dc-ba9c-b81a0e6a5a61 · outbound

This paper cites Probabilistic policy reuse for safe rein- forcement learning.

Combining Trained Models in Reinforcement Learning Probabilistic policy reuse for safe rein- forcement learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.948925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 72cf85ad-380f-495b-9bdd-eea41a2b3a49 · outbound

This paper cites Policy transfer via skill adaptation and composition.

Combining Trained Models in Reinforcement Learning Policy transfer via skill adaptation and composition

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.956442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 9a6f5fb6-fbd1-4ec5-b5b2-e609551f54bc · outbound

This paper cites Transfer reinforcement learning based on gaussian process policy reuse.

Combining Trained Models in Reinforcement Learning Transfer reinforcement learning based on gaussian process policy reuse

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.936586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 395cfb41-45e4-45d7-8348-9a21c60215bc · outbound

This paper cites Safe adaptive policy transfer reinforcement learning for distributed multiagent control.

Combining Trained Models in Reinforcement Learning Safe adaptive policy transfer reinforcement learning for distributed multiagent control

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.927512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation cf5c622e-75ed-474d-a2ad-5d391c70d17c · outbound

This paper cites Combining pre-trained models for enhanced feature representation in reinforcement learning.

Combining Trained Models in Reinforcement Learning Combining pre-trained models for enhanced feature representation in reinforcement learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.881768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation b3d3b3bd-53d2-487c-af70-ed71c8814ece · outbound

This paper cites The PRISMA 2020 statement: an updated guideline for reporting systematic reviews.

Combining Trained Models in Reinforcement Learning The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.921673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 3df149d1-beea-422c-a5be-585ba198f041 · outbound

This paper cites Parallel reinforcement learning: a framework and case study.

Combining Trained Models in Reinforcement Learning Parallel reinforcement learning: a framework and case study

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.952915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 8a93cf03-1d56-492c-89df-bc5c2e46204e · outbound

This paper cites Policy distillation and value matching in multiagent reinforcement learning.

Combining Trained Models in Reinforcement Learning Policy distillation and value matching in multiagent reinforcement learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.907657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation cbd5881f-28e7-42b4-8687-97a61a3a865c · outbound

This paper cites Leaders and collaborators: Address- ing sparse reward challenges in multi-agent reinforcement learning.

Combining Trained Models in Reinforcement Learning Leaders and collaborators: Address- ing sparse reward challenges in multi-agent reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.885627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 05cbf563-16a2-4662-8b1c-c91d5ccebc9c · outbound

This paper cites A hybrid ensemble framework for adversarial robustness in deep reinforcement learning.

Combining Trained Models in Reinforcement Learning A hybrid ensemble framework for adversarial robustness in deep reinforcement learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.900467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Observation 44c4ab00-64e0-470c-80cb-3c77b6083ba8 · outbound

This paper cites PEARL: FPGA-based reinforcement learning acceleration with pipelined parallel environments.

Combining Trained Models in Reinforcement Learning PEARL: FPGA-based reinforcement learning acceleration with pipelined parallel environments

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T03:01:33.963500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-23T06:31:01.910684+00:00.

Pith citing papers

No inbound Pith citation observations are available.