Paper Citation Record · LEDGER

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control

As of 14 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2507.02712.

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

pith.paper-citation-record.v1
2507.02712 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:31:07.893666Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:48:47.418815Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T00:48:47.605133Z

Reference resolution

54 of 54 outbound references displayed

verified exact7
verified fuzzy10
unresolved36
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7aff1d63-812c-4418-abc2-8d8de1be267e · outbound

This paper cites G., Martinez-Canabal, A., Restivo, L., Yiu, A.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control G., Martinez-Canabal, A., Restivo, L., Yiu, A

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.752935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 14692d07-0f6c-49d5-a1e6-f00270608c45 · outbound

This paper cites an unresolved cited work.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:31:08.742771Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 076d37ad-e04c-42d6-84eb-381e891b9d0e · outbound

This paper cites Hindsight Experience Replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Hindsight Experience Replay

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.729165Z

Source-reported events for the cited work

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Observation 83eafc08-8570-48de-af84-d8c83a37784d · outbound

This paper cites Towards Deeper Deep Reinforcement Learning with Spectral Normalization.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Towards Deeper Deep Reinforcement Learning with Spectral Normalization

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:31:08.559364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a1146ce5-7daf-4a84-a69c-1cb359dba087 · outbound

This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.736250Z

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Observation 99ca0aa0-a4d3-42e7-b55f-718c6b1edc7f · outbound

This paper cites Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:08.527278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 87541af9-5a2f-49b7-b6b7-f11c5d8562f6 · outbound

This paper cites G., and Courville, A.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control G., and Courville, A

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.731769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6be13383-186a-45cb-a57a-3a43ca0e5d28 · outbound

This paper cites Revisiting fundamentals of experience replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Revisiting fundamentals of experience replay

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.721805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 05813f0f-6324-4cd3-a5c3-ea08cd96f953 · outbound

This paper cites Addressing function approximation error in actor-critic methods.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Addressing function approximation error in actor-critic methods

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.752348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a01e27ca-ae96-4cf7-8ea7-ad628b92dfc6 · outbound

This paper cites Off-Policy Deep Reinforcement Learning without Exploration.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Off-Policy Deep Reinforcement Learning without Exploration

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.755855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a438761-5c34-46cc-ab07-baf253aa120c · outbound

This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.759082Z

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Observation f8a51ae0-cfbb-42c8-8313-ccb38a545b0f · outbound

This paper cites Mastering Diverse Domains through World Models.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Mastering Diverse Domains through World Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.762140Z

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Observation b235f8dc-118b-4247-878d-df8dc60994be · outbound

This paper cites On the role of planning in model-based deep reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control On the role of planning in model-based deep reinforcement learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.765568Z

Source-reported events for the cited work

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Observation 0b92d735-cce7-4368-bea6-8f53734a18e5 · outbound

This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.768698Z

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Observation 7644f8c5-a2b8-4ba6-b0bb-8d0aab9341c1 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Deep Learning Scaling is Predictable, Empirically

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.771743Z

Source-reported events for the cited work

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Observation ca392238-6909-4c9c-b9fb-c250139f0bcd · outbound

This paper cites an unresolved cited work.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:31:08.704544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d83cb6d7-1eef-42bd-996c-db7aca6e49d4 · outbound

This paper cites Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes

Reference 17

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Observation e7cfef08-df5d-406c-a6cb-6f94a450dca7 · outbound

This paper cites Maintaining Plasticity in Continual Learning via Regenerative Regularization.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.781548Z

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Observation d088f7c7-5f19-4ea5-a0b3-35d00bd2887e · outbound

This paper cites Maxmin Q-learning: Controlling the Estimation Bias of Q-learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Maxmin Q-learning: Controlling the Estimation Bias of Q-learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:08.425853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 60bae0e6-b729-41ab-8dbe-315c98a2bd88 · outbound

This paper cites Crafting papers on machine learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Crafting papers on machine learning

Reference 20

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Observation f4fbd433-1d8f-41b2-8366-2add8b07afcf · outbound

This paper cites SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.791190Z

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Observation 509a2ea9-5c56-428c-9e8e-d5fa44404ded · outbound

This paper cites Multi-Game Decision Transformers.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Multi-Game Decision Transformers

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.794565Z

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Observation 06716c5a-e090-4980-9555-a23967eca3b6 · outbound

This paper cites Efficient Deep Reinforcement Learning Requires Regulating Overfitting.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.797950Z

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Observation c26ec12c-de27-401b-a85f-ce66137a5902 · outbound

This paper cites Self-improving reactive agents based on reinforcement learning, planning and teaching.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Self-improving reactive agents based on reinforcement learning, planning and teaching

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.686847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d32c5df-e889-44bd-8c6f-7f0abf8b5fbc · outbound

This paper cites Neuroplastic Expansion in Deep Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Neuroplastic Expansion in Deep Reinforcement Learning

Reference 25

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Source-reported events for the cited work

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Observation 68e96471-c61d-43a5-bcce-8d2ef2b968b1 · outbound

This paper cites Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:08.369997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 21ad5a78-4195-4b9f-81e7-a00887f5abe9 · outbound

This paper cites Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:08.356162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e10a8a42-796e-4cff-8630-84e19176cb1e · outbound

This paper cites Revisiting plasticity in visual reinforcement learning: Data.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Revisiting plasticity in visual reinforcement learning: Data

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.676422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 49ae2da8-2275-4f2f-87f6-c2ee1728b98b · outbound

This paper cites Learning better with less: effective augmentation for sample-efficient visual reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Learning better with less: effective augmentation for sample-efficient visual reinforcement learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.665608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ee022c66-5efd-4700-ab41-d07e51ad42f9 · outbound

This paper cites A., Veness, J., Bellemare, M.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control A., Veness, J., Bellemare, M

Reference 30

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Observation 5c2c47d7-9055-4489-9486-cf68f70856f4 · outbound

This paper cites Tactical Optimism and Pessimism for Deep Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Tactical Optimism and Pessimism for Deep Reinforcement Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.823397Z

Source-reported events for the cited work

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Observation 17b91c07-a928-4fb7-8da3-3047956666e2 · outbound

This paper cites Safe and efficient off-policy reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Safe and efficient off-policy reinforcement learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.646868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e48555d3-1a4f-4afc-9fb0-98007b4db202 · outbound

This paper cites Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.829896Z

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Observation 06aacba1-9960-461d-af50-c9385de58593 · outbound

This paper cites Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 34

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Observation f7ec94b5-f792-475f-8695-a22c52df0d42 · outbound

This paper cites The primacy bias in deep reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control The primacy bias in deep reinforcement learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.636343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation de80d9de-0e39-4ad9-ad13-2bf944c18a31 · outbound

This paper cites The primacy bias in deep reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control The primacy bias in deep reinforcement learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.626016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cace0f27-cf37-4aed-8e64-ca26e1e72b3b · outbound

This paper cites Deep Reinforcement Learning with Plasticity Injection.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Deep Reinforcement Learning with Plasticity Injection

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:08.203321Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation af5cebe9-6f5d-4b4f-9a91-c9485cf6dc85 · outbound

This paper cites F., Maximo, M.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control F., Maximo, M

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:31:08.615876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d21eff04-0311-410b-b076-d73df4cfcf2b · outbound

This paper cites Mind the Model, Not the Agent: The Primacy Bias in Model-based RL.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Mind the Model, Not the Agent: The Primacy Bias in Model-based RL

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:08.189091Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 00063d9b-4edb-4755-a226-0234e1737fcb · outbound

This paper cites Prioritized Experience Replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Prioritized Experience Replay

Reference 40

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Observation 32c85167-3414-4d21-9124-6089bf9e1e07 · outbound

This paper cites Bigger, Better, Faster: Human-level Atari with human-level efficiency.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Bigger, Better, Faster: Human-level Atari with human-level efficiency

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.855211Z

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Observation 1b531b16-792b-455e-815a-2354069bbe5a · outbound

This paper cites HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.858169Z

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Observation 3fef00f0-d673-41eb-86b8-904c818345a2 · outbound

This paper cites D2RL: Deep Dense Architectures in Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control D2RL: Deep Dense Architectures in Reinforcement Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.861099Z

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Observation 4e135d2c-82fc-4882-9e82-6d8b96c4b333 · outbound

This paper cites S., and Evci, U.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control S., and Evci, U

Reference 44

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Observation f04a19ad-6483-4bac-a13d-214ccc1c524b · outbound

This paper cites Model-based off-policy deep reinforcement learning with model-embedding.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Model-based off-policy deep reinforcement learning with model-embedding

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.867001Z

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Observation a3b7639b-7c2d-4fb1-9739-cf884d6fc7b2 · outbound

This paper cites DeepMind Control Suite.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control DeepMind Control Suite

Reference 46

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Observation 5cdb96e0-727c-4c76-a696-61ebe68dd034 · outbound

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

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Mujoco: A physics engine for model-based control

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.873149Z

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Observation 36c73bf8-76c9-416b-a0de-3420f9133fa1 · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Deep Reinforcement Learning and the Deadly Triad

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.875903Z

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Observation 3de77387-dce0-45db-97f7-38563c7bb890 · outbound

This paper cites DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.879014Z

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Observation aec07206-9c07-44b4-a215-54ef911545a2 · outbound

This paper cites an unresolved cited work.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Unresolved cited work

Reference 50

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Observation d999a541-9b98-4a60-b247-3d673b20e49e · outbound

This paper cites CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-06T20:31:07.940764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 09a809a3-306b-43d0-a0ad-40d4b16a8723 · outbound

This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.888111Z

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Observation 9d297b55-e8b9-4877-bdd8-16220fb3e746 · outbound

This paper cites A Deeper Look at Experience Replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control A Deeper Look at Experience Replay

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T20:31:07.890842Z

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Observation f0155f89-63b6-4af6-9489-8b181178e5bb · outbound

This paper cites write newline.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control write newline

Reference 54

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Observation 2a7f7d05-1265-4948-a4e2-7a9279085f49 · inbound

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control cites this paper.

V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control

Reference 293

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