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Paper Citation Record · LEDGER

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2506.21899.

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

pith.paper-citation-record.v1
2506.21899 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:18:43.007244Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:31:37.407842Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T00:31:42.433375Z

Reference resolution

36 of 36 outbound references displayed

  • verified exact3
  • verified fuzzy4
  • unresolved28
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3afbc043-7ba2-4044-8a4c-38413c801bf9 · outbound

This paper cites Improving out-of-distribution generalization via multi-task self-supervised pretraining.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Improving out-of-distribution generalization via multi-task self-supervised pretraining

Reference 1

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Observation 3533351b-6fac-4e87-9bef-9349b9ab5117 · outbound

This paper cites an unresolved cited work.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 4

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 1fd94e55-4c98-44fd-be41-e5f1d093587e · outbound

This paper cites Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Reference 7

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Observation a539f7f3-7c5e-490c-946e-f74f006b6410 · outbound

This paper cites Continual learning with hypernetworks.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Continual learning with hypernetworks

Reference 10

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source=pdf_text observed=2026-08-06T22:18:42.628063Z digest=sha256:13fdbbdf82d823db9e6029652f6f36a1a786a09844481cfe1564ae7f7243e73d

Observation 3f23b6e9-4c71-4161-a150-7a224a241acd · outbound

This paper cites an unresolved cited work.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 11

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c42d85a1-c796-451a-ad83-b78804aa0056 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Distilling the Knowledge in a Neural Network

Reference 12

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Observation 55fe612a-b286-42ee-be1e-97d874ec0488 · outbound

This paper cites L2Explorer: A Lifelong Reinforcement Learning Assessment Environment.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review L2Explorer: A Lifelong Reinforcement Learning Assessment Environment

Reference 13

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Observation 77c2f2ef-5336-4e8d-b3f1-9b9399ff13fb · outbound

This paper cites an unresolved cited work.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 18

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Observation 65ccaf9b-3ff0-4f96-a0fd-e02f54e61df3 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 19

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Observation fc802a13-58f7-4e2b-ab80-797f20d061e4 · outbound

This paper cites LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Reference 20

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Observation d586cc1d-2493-481c-8325-29f6209e9b9d · outbound

This paper cites Reset-Free Lifelong Learning with Skill-Space Planning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Reset-Free Lifelong Learning with Skill-Space Planning

Reference 21

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Observation ce0c81c4-14b0-45f9-bc92-21fef39edf7f · outbound

This paper cites Parameter-Level Soft-Masking for Continual Learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Parameter-Level Soft-Masking for Continual Learning

Reference 22

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Observation d54f284d-42aa-4bc3-986f-cb9be77a2a53 · outbound

This paper cites L., Kafle, K., Shrestha, R., Acharya, M., and Kanan, C.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review L., Kafle, K., Shrestha, R., Acharya, M., and Kanan, C

Reference 23

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c9f56471-f178-4cf6-b4dc-c291ad8267eb · outbound

This paper cites Modular Lifelong Reinforcement Learning via Neural Composition.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Modular Lifelong Reinforcement Learning via Neural Composition

Reference 24

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Observation d3349ab9-dcd8-4e83-9ced-ed4eb7fd9668 · outbound

This paper cites P., Chakravarthi Raja, S., Cheney, N., Clune, J., et al.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review P., Chakravarthi Raja, S., Cheney, N., Clune, J., et al

Reference 25

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2791e104-cf82-437b-bf56-916edc5caaa2 · outbound

This paper cites Sequoia: A Software Framework to Unify Continual Learning Research.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Sequoia: A Software Framework to Unify Continual Learning Research

Reference 26

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Observation 6440835a-8b5f-4aa6-b4c6-2bcfd7f674bb · outbound

This paper cites HyperNetworks.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review HyperNetworks

Reference 27

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Observation 61416631-1ccd-4ee5-9c24-1435acd5d9a6 · outbound

This paper cites an unresolved cited work.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 28

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Observation e9f99204-41a3-437e-8476-ffb3a9d66bdd · outbound

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Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 29

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Observation 82d61719-4f04-493b-a46d-37ee4456811d · outbound

This paper cites an unresolved cited work.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 30

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Observation 341d775a-ba4c-451d-a430-29a8848e3cda · outbound

This paper cites Learn the Time to Learn: Replay Scheduling in Continual Learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Learn the Time to Learn: Replay Scheduling in Continual Learning

Reference 31

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Observation a5972eb0-5e95-4ae6-8f4b-a5e200ad9514 · outbound

This paper cites and Precup, D.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review and Precup, D

Reference 32

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Observation 17f2c61a-b793-47d7-ae35-d03b27099ead · outbound

This paper cites Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning

Reference 33

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Observation 14b9d049-2138-473d-a3c8-3f9e753714f6 · outbound

This paper cites The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning

Reference 34

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Observation 771be523-93df-4d0b-8566-fb733aaa4021 · outbound

This paper cites DisCoRL: Continual Reinforcement Learning via Policy Distillation.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review DisCoRL: Continual Reinforcement Learning via Policy Distillation

Reference 35

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Observation 6db502ad-3ff6-45cf-a6c2-a291bebf508c · outbound

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Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 36

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Observation 9fd92b3e-7822-43a6-82b3-66e41d9fd0a5 · outbound

This paper cites an unresolved cited work.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Unresolved cited work

Reference 37

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Observation 50e114ff-dcc8-4046-b8c3-c78621c2cc3e · outbound

This paper cites Lifelong Domain Word Embedding via Meta-Learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Lifelong Domain Word Embedding via Meta-Learning

Reference 38

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation cc95c3f8-4eb8-43b2-9e89-4a23a3894ee2 · outbound

This paper cites Lifelong Learning with Dynamically Expandable Networks.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Lifelong Learning with Dynamically Expandable Networks

Reference 39

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Observation 5f9d93c3-0911-4c03-a771-3d2ac4b34cd7 · outbound

This paper cites MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

Reference 40

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Observation d290b41b-0aa6-4cf2-ade4-e8e66a334bbf · outbound

This paper cites Generative replay with feedback connections as a general strategy for continual learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Generative replay with feedback connections as a general strategy for continual learning

Reference 82

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Observation 4b953f3b-0946-4f92-b650-8f860f1d8009 · outbound

This paper cites DeepMind Lab.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review DeepMind Lab

Reference 307

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Observation 2114d0a5-64be-4a9a-b54e-df9c0760011f · outbound

This paper cites Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

Reference 389

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Observation 501d933f-7719-492a-817a-d2b1d479df52 · outbound

This paper cites D., Jeong, J., and Kim, G.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review D., Jeong, J., and Kim, G

Reference 1476

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 2a35911e-cf04-40f7-850e-bf7254c3a1de · outbound

This paper cites Efficient Lifelong Learning with A-GEM.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Efficient Lifelong Learning with A-GEM

Reference 1724

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Observation e58bb50c-5338-4da9-a3e7-54726ced6629 · outbound

This paper cites Building a Subspace of Policies for Scalable Continual Learning.

Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review Building a Subspace of Policies for Scalable Continual Learning

Reference 4309

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source=pdf_text observed=2026-08-06T22:18:39.117624Z digest=sha256:a5503086d4eb70963d4fc79ea40c0821ec85a6fba63ed359fe9d3fd1256eadb0

Pith citing papers

Observation a05dd6eb-fc75-48ef-a39f-ab43b4018c90 · inbound

PATH-Bench: Path-Dependent Evaluation of Lifelong Agents cites this paper.

PATH-Bench: Path-Dependent Evaluation of Lifelong Agents Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review

Reference 19

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local_arxiv, observed 2026-08-06T00:31:42.542901Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T00:31:37.407842Z digest=sha256:29b9cc8c4d3a816a390af0bfe8b7f7538af18dbfb6a9d142429c76be93348958