Paper Citation Record · LEDGER

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model

As of 13 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:2501.12627.

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

pith.paper-citation-record.v1
2501.12627 v1

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:04:31.896668Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:15:08.304150Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:09:40.723680Z

Reference resolution

47 of 47 outbound references displayed

verified exact1
verified fuzzy36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bd12ae0c-9e7c-47c1-ba72-949a942c50d9 · outbound

This paper cites Deep reinforcement learning at the edge of the statistical precipice.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Deep reinforcement learning at the edge of the statistical precipice

Reference 1

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Observation 777d5ba1-acf0-467c-9d2e-420e08bb6f61 · outbound

This paper cites Atari-5: Distilling the arcade learning environment down to five games.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Atari-5: Distilling the arcade learning environment down to five games

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.655050Z

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Observation b025b990-4e9d-4136-91ad-7e30cf51b870 · outbound

This paper cites Existence, relatedness, and growth: Human needs in organizational settings.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Existence, relatedness, and growth: Human needs in organizational settings

Reference 3

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Observation fab2e43a-96cf-48ae-900d-3b37447c7a19 · outbound

This paper cites Using confidence bounds for exploitation-exploration trade-offs.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Using confidence bounds for exploitation-exploration trade-offs

Reference 4

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Observation 42f52b22-413a-4239-ac7e-ae3ce297336c · outbound

This paper cites Never give up: Learning directed exploration strategies.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Never give up: Learning directed exploration strategies

Reference 5

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Observation dc46cc3b-895d-4b3d-8121-fd8c24dbfd99 · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model The arcade learning environment: An evaluation platform for general agents

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.593250Z

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Observation 1553f4ef-748c-4999-a2d7-b454bfa9b33e · outbound

This paper cites Unifying count-based exploration and intrinsic motivation.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Unifying count-based exploration and intrinsic motivation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.578496Z

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Observation 29042ab4-e56a-47ea-a5dc-495eafe0d9fc · outbound

This paper cites A markovian decision process.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model A markovian decision process

Reference 8

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Observation 9d1afa23-e649-41e7-83cf-a20d2800927d · outbound

This paper cites Exploration by random network distillation.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Exploration by random network distillation

Reference 9

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Source-reported events for the cited work

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Observation 80fddff9-f795-4460-b351-96a5501b9a18 · outbound

This paper cites Explore, discover and learn: Unsupervised discovery of state-covering skills.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Explore, discover and learn: Unsupervised discovery of state-covering skills

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.531059Z

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Observation 10b4dfd5-e1dd-453f-95c2-e8e02292dbfc · outbound

This paper cites Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.514663Z

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Observation 85ba021e-a615-4c9e-9059-04ffd982424a · outbound

This paper cites Leveraging procedural generation to benchmark reinforcement learning.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Leveraging procedural generation to benchmark reinforcement learning

Reference 12

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Observation b48be9ad-2e89-446a-857f-a7f686b64864 · outbound

This paper cites Stochastic linear optimization under bandit feedback.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Stochastic linear optimization under bandit feedback

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.479715Z

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Observation 0dabe982-1812-4302-9b1a-383c4fc6b0af · outbound

This paper cites Diversity is all you need: Learning skills without a reward function.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Diversity is all you need: Learning skills without a reward function

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.464273Z

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Observation d996408c-e6e1-41f5-9480-c491fba766b4 · outbound

This paper cites Adversarially guided actor-critic.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Adversarially guided actor-critic

Reference 15

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Observation c02a526d-a851-4081-a54a-850e1efcf554 · outbound

This paper cites Variational Intrinsic Control.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Variational Intrinsic Control

Reference 16

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Observation 02f8d2c1-69c4-40ef-b290-4a966b455c1f · outbound

This paper cites Fast task inference with variational intrinsic successor features.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Fast task inference with variational intrinsic successor features

Reference 17

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Observation 40c39387-4a1a-4ff8-a700-7ce1765cfaae · outbound

This paper cites Provably efficient maximum entropy exploration.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Provably efficient maximum entropy exploration

Reference 18

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Source-reported events for the cited work

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Observation 6c77159e-7fab-45ef-a465-6146559a7f82 · outbound

This paper cites Exploration via elliptical episodic bonuses.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Exploration via elliptical episodic bonuses

Reference 19

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Source-reported events for the cited work

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Observation ecdcb10c-28dd-4b67-8e33-85840a9f89e1 · outbound

This paper cites A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs

Reference 20

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Observation 726615b0-541b-47b7-85c3-e284cc80958b · outbound

This paper cites Planning and acting in partially observable stochastic domains.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Planning and acting in partially observable stochastic domains

Reference 21

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Source-reported events for the cited work

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Observation bb2441da-0e0b-4c45-b9b7-92c3c81f1039 · outbound

This paper cites Curl: Contrastive unsupervised representations for reinforcement learning.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Curl: Contrastive unsupervised representations for reinforcement learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.365128Z

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Observation 2b80cb0b-265f-46b1-a407-77add88a25d5 · outbound

This paper cites Cic: Contrastive intrinsic control for unsupervised skill discovery.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Cic: Contrastive intrinsic control for unsupervised skill discovery

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.349486Z

Source-reported events for the cited work

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Observation 0b57d581-455a-4b5d-9b79-286e760ea403 · outbound

This paper cites Urlb: Unsupervised reinforcement learning benchmark.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Urlb: Unsupervised reinforcement learning benchmark

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.333107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fbfafc83-6a9c-4275-aec1-51134eeed3cd · outbound

This paper cites A contextual-bandit approach to personalized news article recommendation.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model A contextual-bandit approach to personalized news article recommendation

Reference 25

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Observation 586a0342-e2ce-4c97-a19a-f321767e38ac · outbound

This paper cites Aps: Active pretraining with successor features.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Aps: Active pretraining with successor features

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.305952Z

Source-reported events for the cited work

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Observation f9c13c34-23d2-40fa-9e4e-a9a9e1d6d75e · outbound

This paper cites Count-based exploration with the successor representation.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Count-based exploration with the successor representation

Reference 27

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Source-reported events for the cited work

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Observation 167bf6db-1c33-49a3-a2db-506bafe739d2 · outbound

This paper cites A dynamic theory of human motivation.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model A dynamic theory of human motivation

Reference 28

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Source-reported events for the cited work

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Observation 3e37be31-db05-4499-93aa-fec1ee4c345b · outbound

This paper cites Improving intrinsic exploration with language abstractions.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Improving intrinsic exploration with language abstractions

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.258772Z

Source-reported events for the cited work

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Observation f0e6ea44-e187-4119-b82d-106f8e2f4acb · outbound

This paper cites Count-based exploration with neural density models.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Count-based exploration with neural density models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.242416Z

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Observation 486ab38a-5025-4edb-b7e2-680f42fbb893 · outbound

This paper cites Lipschitz-constrained Unsupervised Skill Discovery.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Lipschitz-constrained Unsupervised Skill Discovery

Reference 31

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Observation d051dbc2-2728-4121-bad0-cf8043d7f72c · outbound

This paper cites Curiosity-driven exploration by self-supervised prediction.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Curiosity-driven exploration by self-supervised prediction

Reference 32

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This paper cites Self-supervised exploration via disagreement.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Self-supervised exploration via disagreement

Reference 33

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Observation c253f65d-1884-4552-b4aa-34d7aeb1d127 · outbound

This paper cites Ride: Rewarding impact-driven exploration for procedurally-generated environments.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Ride: Rewarding impact-driven exploration for procedurally-generated environments

Reference 34

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Observation e7f619b6-92d2-4171-836b-d697f8a232cb · outbound

This paper cites Minihack the planet: A sandbox for open-ended reinforcement learning research.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Minihack the planet: A sandbox for open-ended reinforcement learning research

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.182309Z

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Observation 51ccc0d7-1fd0-4e2b-b73b-846d65d96fa9 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Proximal Policy Optimization Algorithms

Reference 36

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This paper cites State entropy maximization with random encoders for efficient exploration.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model State entropy maximization with random encoders for efficient exploration

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.166131Z

Source-reported events for the cited work

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Observation 3bd73490-d8c9-4f91-baf4-0b9fe55ed8fc · outbound

This paper cites Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models

Reference 38

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This paper cites Reinforcement learning: An introduction.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Reinforcement learning: An introduction

Reference 39

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This paper cites \# exploration: A study of count-based exploration for deep reinforcement learning.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model \# exploration: A study of count-based exploration for deep reinforcement learning

Reference 40

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Observation b2bcbb78-6710-494f-8f9a-a84688d5851a · outbound

This paper cites Reinforcement learning with prototypical representations.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Reinforcement learning with prototypical representations

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.127782Z

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Observation ce6a0bb6-ad98-4bdf-8008-b911a70e1984 · outbound

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

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Rewarding episodic visitation discrepancy for exploration in reinforcement learning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.105311Z

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Observation 3f4e8c4b-f613-45e3-8860-13a13a60dd75 · outbound

This paper cites R \'e nyi state entropy maximization for exploration acceleration in reinforcement learning.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model R \'e nyi state entropy maximization for exploration acceleration in reinforcement learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.089219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f3e1a87a-8f8a-406a-abc1-4777764238e2 · outbound

This paper cites RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model RLeXplore: Accelerating Research in Intrinsically-Motivated Reinforcement Learning

Reference 44

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Observation 892dd3d4-1673-431a-b114-762c796b0303 · outbound

This paper cites Rllte: Long-term evolution project of reinforcement learning.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Rllte: Long-term evolution project of reinforcement learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.072848Z

Source-reported events for the cited work

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Observation c9f54259-2184-4fac-8134-bcbf9206621d · outbound

This paper cites Noveld: A simple yet effective exploration criterion.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model Noveld: A simple yet effective exploration criterion

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:04:32.057202Z

Source-reported events for the cited work

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Observation a0e2ef72-6f7e-4268-8b52-866ea9ee5639 · outbound

This paper cites write newline.

Deep Reinforcement Learning with Hybrid Intrinsic Reward Model write newline

Reference 47

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Pith citing papers

Observation ea666f1f-983f-42cd-9966-94b2d78a1c1d · inbound

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning cites this paper.

Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning Deep Reinforcement Learning with Hybrid Intrinsic Reward Model

Reference 251

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:09:40.724925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.