Paper Citation Record · LEDGER

Generalizing from a few environments in safety-critical reinforcement learning

As of 19 July 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:1907.01475.

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

pith.paper-citation-record.v1
1907.01475 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T10:57:45.056957Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-19T06:30:13.599613+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-11T01:55:38.554161Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T04:06:00.153706Z

Reference resolution

39 of 39 outbound references displayed

verified exact13
verified fuzzy24
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ace697f5-a5ff-48f0-aa33-81a072063867 · outbound

This paper cites an unresolved cited work.

Generalizing from a few environments in safety-critical reinforcement learning Unresolved cited work

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.425104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation ba89d78e-80c0-47f4-a82b-16ddcff20de1 · outbound

This paper cites Learning Dexterous In-Hand Manipulation.

Generalizing from a few environments in safety-critical reinforcement learning Learning Dexterous In-Hand Manipulation

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.111131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 8b07aa43-0892-438b-a908-c9a377d76961 · outbound

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

Generalizing from a few environments in safety-critical reinforcement learning Using conﬁdence bounds for exploitation-exploration trade-offs.J

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T11:00:40.143521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b6ac8dc2-cf47-48bd-bdff-32914053b6ae · outbound

This paper cites On the optimization of a synaptic learning rule.

Generalizing from a few environments in safety-critical reinforcement learning On the optimization of a synaptic learning rule

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.407692Z

Source-reported events for the cited work

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Observation e4f6e77c-4937-46e6-9261-1cdbda152809 · outbound

This paper cites Quantifying Generalization in Reinforcement Learning.

Generalizing from a few environments in safety-critical reinforcement learning Quantifying Generalization in Reinforcement Learning

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.180526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 0e28254e-a8a4-48b1-930b-afbe521085b0 · outbound

This paper cites Ensemble methods in machine learning.

Generalizing from a few environments in safety-critical reinforcement learning Ensemble methods in machine learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.419915Z

Source-reported events for the cited work

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Observation 9b492b63-79cf-4529-b311-21e1036bac03 · outbound

This paper cites RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning.

Generalizing from a few environments in safety-critical reinforcement learning RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.118463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f2377845-2651-4e65-b59c-4953a600dbbf · outbound

This paper cites Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures.

Generalizing from a few environments in safety-critical reinforcement learning Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.415929Z

Source-reported events for the cited work

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Observation a9fc6219-3c8c-4ac1-a151-91cc96beb823 · outbound

This paper cites Generalization and Regularization in DQN.

Generalizing from a few environments in safety-critical reinforcement learning Generalization and Regularization in DQN

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation aaeb6f5b-3b16-4dc5-9c76-469e97411a32 · outbound

This paper cites Model-agnostic meta-learning for fast adap- tation of deep networks.

Generalizing from a few environments in safety-critical reinforcement learning Model-agnostic meta-learning for fast adap- tation of deep networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.403860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f84add05-a7cb-4f9c-a22f-09d48f7f7f0f · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Generalizing from a few environments in safety-critical reinforcement learning Dropout as a bayesian approximation: Representing model uncertainty in deep learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.387236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f3a85e15-9a8b-472c-a4d7-51a0c9a19224 · outbound

This paper cites A comprehensive survey on safe reinforcement learning.

Generalizing from a few environments in safety-critical reinforcement learning A comprehensive survey on safe reinforcement learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.395915Z

Source-reported events for the cited work

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Observation 5812775e-ecb4-4db0-9c21-5146b374519f · outbound

This paper cites Deep residual learning for image recognition.

Generalizing from a few environments in safety-critical reinforcement learning Deep residual learning for image recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.381674Z

Source-reported events for the cited work

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Observation eb961da4-30f8-4c9d-b932-91b19aeb16c6 · outbound

This paper cites Deep residual learning for image recognition.

Generalizing from a few environments in safety-critical reinforcement learning Deep residual learning for image recognition

Reference 14

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Source-reported events for the cited work

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Observation c609889b-df76-4bb5-9840-0b9818e3c0b4 · outbound

This paper cites Learning to learn using gradient descent.

Generalizing from a few environments in safety-critical reinforcement learning Learning to learn using gradient descent

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.377769Z

Source-reported events for the cited work

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Observation e796d3c7-486d-402d-9682-368ed5c4ffc8 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Generalizing from a few environments in safety-critical reinforcement learning Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.192110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 33b91704-c959-4409-a1e1-7041f15d3bd5 · outbound

This paper cites Uncertainty-Aware Reinforcement Learning for Collision Avoidance.

Generalizing from a few environments in safety-critical reinforcement learning Uncertainty-Aware Reinforcement Learning for Collision Avoidance

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.149786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 9eea0137-9b9a-48ef-b7b7-1a2a89ac5477 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Generalizing from a few environments in safety-critical reinforcement learning Adam: A Method for Stochastic Optimization

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.124658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 819d5d52-be80-4d90-8625-9c69663277a6 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Generalizing from a few environments in safety-critical reinforcement learning Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.411711Z

Source-reported events for the cited work

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Observation 8675d013-b092-4961-b0c8-e6aa96409b3b · outbound

This paper cites AI Safety Gridworlds.

Generalizing from a few environments in safety-critical reinforcement learning AI Safety Gridworlds

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.155281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b1a621cb-991e-4af2-9c61-3b288350a317 · outbound

This paper cites End-to-end training of deep visuomotor policies.

Generalizing from a few environments in safety-critical reinforcement learning End-to-end training of deep visuomotor policies

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.430779Z

Source-reported events for the cited work

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Observation ee5a0c67-f611-4f5f-a074-45735ceb60a5 · outbound

This paper cites End-to-End Task-Completion Neural Dialogue Systems.

Generalizing from a few environments in safety-critical reinforcement learning End-to-End Task-Completion Neural Dialogue Systems

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation f8d3f6ab-ba35-480e-a989-7fdb773a3041 · outbound

This paper cites Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear.

Generalizing from a few environments in safety-critical reinforcement learning Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear

Reference 23

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Source-reported events for the cited work

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Observation cc4dbb46-5c76-4601-8fce-5774da23a8d4 · outbound

This paper cites Evaluating uncertainty quantiﬁca- tion in end-to-end autonomous driving control.

Generalizing from a few environments in safety-critical reinforcement learning Evaluating uncertainty quantiﬁca- tion in end-to-end autonomous driving control

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.399964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 1cddda87-137a-4e17-a438-30e7be8bba6f · outbound

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

Generalizing from a few environments in safety-critical reinforcement learning Human-level control through deep reinforcement learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.391600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 709abace-055f-4d15-a79c-441e6860b4df · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

Generalizing from a few environments in safety-critical reinforcement learning Asynchronous methods for deep reinforcement learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.373816Z

Source-reported events for the cited work

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Observation 26c2877a-0319-4e5c-83d8-52401f47e49f · outbound

This paper cites Automatic differentiation in PyTorch.

Generalizing from a few environments in safety-critical reinforcement learning Automatic differentiation in PyTorch

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.370096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 489e8c78-4b89-4f74-85a8-fd7a3a31f23c · outbound

This paper cites Fingerprint Policy Optimisation for Robust Reinforcement Learning.

Generalizing from a few environments in safety-critical reinforcement learning Fingerprint Policy Optimisation for Robust Reinforcement Learning

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation efcb173d-4b0f-4870-b240-ac0695b60fbe · outbound

This paper cites Trial without error: Towards safe reinforcement learning via human intervention.

Generalizing from a few environments in safety-critical reinforcement learning Trial without error: Towards safe reinforcement learning via human intervention

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.362655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation afa4a596-4181-44d6-a929-252416bf7cd7 · outbound

This paper cites Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-.

Generalizing from a few environments in safety-critical reinforcement learning Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.358900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 7e746f95-03dd-4a61-a4f1-f349f8a2d306 · outbound

This paper cites Proximal policy optimization algorithms.

Generalizing from a few environments in safety-critical reinforcement learning Proximal policy optimization algorithms

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.437103Z

Source-reported events for the cited work

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Observation 3109f077-a562-4d89-b8e8-2fa97a321aee · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Generalizing from a few environments in safety-critical reinforcement learning Mastering the game of go with deep neural networks and tree search

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.350523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 1aebcc8b-9e85-4627-83c3-c2f72aae08d5 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.The Journal of Machine Learning Research, 15(1):1929–1958.

Generalizing from a few environments in safety-critical reinforcement learning Dropout: a simple way to prevent neural networks from overﬁtting.The Journal of Machine Learning Research, 15(1):1929–1958

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.354835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 9fa94293-6ea6-4e4f-b68d-68baefb22cff · outbound

This paper cites Reinforcement learning: An introduction.

Generalizing from a few environments in safety-critical reinforcement learning Reinforcement learning: An introduction

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.366229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation b9100708-9646-448d-80a3-acd5b4716e66 · outbound

This paper cites Learning to learn.

Generalizing from a few environments in safety-critical reinforcement learning Learning to learn

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.341698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 732fcf5c-48ed-4c98-92ba-c38fdd3dfba9 · outbound

This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.

Generalizing from a few environments in safety-critical reinforcement learning Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.345626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation bd6ea575-9e6a-47ea-8d6f-ec74e1fabdb6 · outbound

This paper cites Learning to reinforcement learn.

Generalizing from a few environments in safety-critical reinforcement learning Learning to reinforcement learn

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.161709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation 329ed4e3-01d9-4993-8114-cf7e5652fb4a · outbound

This paper cites Q-learning.

Generalizing from a few environments in safety-critical reinforcement learning Q-learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T11:00:41.441134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Observation ce48b6c7-135f-4183-a1e3-48c26b143b65 · outbound

This paper cites A Study on Overfitting in Deep Reinforcement Learning.

Generalizing from a few environments in safety-critical reinforcement learning A Study on Overfitting in Deep Reinforcement Learning

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-05-25T11:00:40.136485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.

Pith citing papers

Observation 8a6ec8de-5328-49be-be50-5ecd91801781 · inbound

Why Does Agentic Safety Fail to Generalize Across Tasks? cites this paper.

Why Does Agentic Safety Fail to Generalize Across Tasks? Generalizing from a few environments in safety-critical reinforcement learning

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-07-04T23:45:52.995688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-19T06:30:13.599613+00:00.