Paper Citation Record · LEDGER

Adaptive Data Exploitation in Deep Reinforcement Learning

As of 10 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2501.12620.

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

pith.paper-citation-record.v1
2501.12620 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:04:46.991531Z

measured 17 of 17 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

17 of 17 outbound references displayed

verified exact0
verified fuzzy10
unresolved6
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f2a4f0d-9d03-48c4-b149-e1c74b208cc8 · outbound

This paper cites For the model update phase, the computational overhead is Oupdate = Oforward + Obackward, (15) where Oforward = Obs1 ∗ B ∗ Nbatches ∗ Nupdate epochs (16) and Obackward = Oforward ∗.

Adaptive Data Exploitation in Deep Reinforcement Learning For the model update phase, the computational overhead is Oupdate = Oforward + Obackward, (15) where Oforward = Obs1 ∗ B ∗ Nbatches ∗ Nupdate epochs (16) and Obackward = Oforward ∗

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.098382Z

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 65ed9f9b-8195-4b85-944d-24baca67a081 · outbound

This paper cites an unresolved cited work.

Adaptive Data Exploitation in Deep Reinforcement Learning Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:04:47.080523Z

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 a89d4666-b8f9-41cd-91a4-69b343c95dbe · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Adaptive Data Exploitation in Deep Reinforcement Learning Dropout: a simple way to prevent neural networks from overfitting

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T17:04:46.939324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d40834fd-d09a-4ba2-bdc1-9e44facb1add · outbound

This paper cites Rewarding episodic visitation discrepancy for exploration in reinforcement learning.

Adaptive Data Exploitation in Deep Reinforcement Learning Rewarding episodic visitation discrepancy for exploration in reinforcement learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.218309Z

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 333312ed-983b-4693-880c-71b6b4fedfbf · outbound

This paper cites 39 Adaptive Data Exploitation in Deep Reinforcement Learning G.

Adaptive Data Exploitation in Deep Reinforcement Learning 39 Adaptive Data Exploitation in Deep Reinforcement Learning G

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.116608Z

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 fe75fc48-d6e6-479d-87b7-6fa62bc6a6fe · outbound

This paper cites For each Procgen environment, Table 2 lists the best augmentation method of DrAC as reported in (Raileanu & Fergus, 2021).

Adaptive Data Exploitation in Deep Reinforcement Learning For each Procgen environment, Table 2 lists the best augmentation method of DrAC as reported in (Raileanu & Fergus, 2021)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.167648Z

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 fb566a84-2e95-438d-85f2-fc2e5af83878 · outbound

This paper cites an unresolved cited work.

Adaptive Data Exploitation in Deep Reinforcement Learning Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-10T17:04:47.150079Z

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 259c1f85-2aa2-489f-ab05-dcbad524c1d4 · outbound

This paper cites In this part, we use the official implementation (Raileanu et al.,.

Adaptive Data Exploitation in Deep Reinforcement Learning In this part, we use the official implementation (Raileanu et al.,

Reference 255

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.184069Z

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 9c485d65-63f3-4807-99df-0f1f09e34cf8 · outbound

This paper cites Master- ing visual continuous control: Improved data-augmented reinforcement learning.

Adaptive Data Exploitation in Deep Reinforcement Learning Master- ing visual continuous control: Improved data-augmented reinforcement learning

Reference 1988

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.233746Z

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 87514cc3-0225-44c3-b39f-58edfedd4a4a · outbound

This paper cites Badia, A.

Adaptive Data Exploitation in Deep Reinforcement Learning Badia, A

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-10T17:04:46.906488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ca56ebb-5817-4acc-81da-c4e7c5225dc4 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Adaptive Data Exploitation in Deep Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-10T17:04:46.934094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95a66473-883c-4354-9c20-b71e6da1e9b8 · outbound

This paper cites Prioritized Experience Replay.

Adaptive Data Exploitation in Deep Reinforcement Learning Prioritized Experience Replay

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-10T17:04:46.928969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ca8056b-83fc-42b3-a864-a3be104dc8ee · outbound

This paper cites The policy loss is defined as: Lπ(θ) = −Eτ ∼π [min (ρt(θ)At, clip (ρt(θ), 1 − ϵ, 1 + ϵ) At)] , (7) where ρt(θ) = πθ(at|st) πθold (at|st) , (8) and ϵ is a clipping range coefficient.

Adaptive Data Exploitation in Deep Reinforcement Learning The policy loss is defined as: Lπ(θ) = −Eτ ∼π [min (ρt(θ)At, clip (ρt(θ), 1 − ϵ, 1 + ϵ) At)] , (7) where ρt(θ) = πθ(at|st) πθold (at|st) , (8) and ϵ is a clipping range coefficient

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.202730Z

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 6e537524-2daf-403f-baa6-79ea88b20ef2 · outbound

This paper cites W., Hilton, J., Klimov, O., and Schulman, J.

Adaptive Data Exploitation in Deep Reinforcement Learning W., Hilton, J., Klimov, O., and Schulman, J

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.276136Z

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 bfe265ff-ab1a-4d37-8bf0-a4fc32e9d83c · outbound

This paper cites and Bai, Y.

Adaptive Data Exploitation in Deep Reinforcement Learning and Bai, Y

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.260283Z

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 a98bf03c-5a33-4f71-9165-891d7b25f8fe · outbound

This paper cites Then we test the PPO agent with three ADEPT algorithms.

Adaptive Data Exploitation in Deep Reinforcement Learning Then we test the PPO agent with three ADEPT algorithms

Reference 2022

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T17:04:47.133881Z

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 85907215-7784-43fc-b120-2d3353e04b07 · outbound

This paper cites Lever- aging procedural generation to benchmark reinforcement learning.

Adaptive Data Exploitation in Deep Reinforcement Learning Lever- aging procedural generation to benchmark reinforcement learning

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:47.291854Z

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.

Pith citing papers

No inbound Pith citation observations are available.