Paper Citation Record · LEDGER

Learning to be Safe: Deep RL with a Safety Critic

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2010.14603.

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

pith.paper-citation-record.v1
2010.14603 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:49.671135Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:58:02.717963Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cc739eb6-004b-48cc-8562-044e20f775e4 · inbound

Analyzing Adversarial Inputs in Deep Reinforcement Learning cites this paper.

Analyzing Adversarial Inputs in Deep Reinforcement Learning Learning to be Safe: Deep RL with a Safety Critic

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:43:50.384230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bd84dcf8-af04-4f21-9831-fa8dc0cf1e55 · inbound

TRAM: Test-Time Risk Adaptation with Mixture of Agents cites this paper.

TRAM: Test-Time Risk Adaptation with Mixture of Agents Learning to be Safe: Deep RL with a Safety Critic

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:15:49.954935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aa990a63-3cce-495c-a024-54dd8ea96872 · inbound

Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks cites this paper.

Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks Learning to be Safe: Deep RL with a Safety Critic

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:35:49.671135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95a59791-72a1-4c78-98fe-8bcaf0996d70 · inbound

SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation cites this paper.

SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation Learning to be Safe: Deep RL with a Safety Critic

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:13.852615Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d4407f7-b0d4-42f5-8a68-7a944c1609c3 · inbound

ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks cites this paper.

ARMOR: Robust Reinforcement Learning-based Control for UAVs under Physical Attacks Learning to be Safe: Deep RL with a Safety Critic

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:10:20.303315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6ae4c67-86a2-4f32-a6ef-f5bc2a766bd4 · inbound

Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis cites this paper.

Safe and Performant Deployment of Autonomous Systems via Model Predictive Control and Hamilton-Jacobi Reachability Analysis Learning to be Safe: Deep RL with a Safety Critic

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T21:50:28.564114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b7fbe78-aa9b-495a-bb71-aa86796ca936 · inbound

Safe and Performant Controller Synthesis using Gradient-based Model Predictive Control and Control Barrier Functions cites this paper.

Safe and Performant Controller Synthesis using Gradient-based Model Predictive Control and Control Barrier Functions Learning to be Safe: Deep RL with a Safety Critic

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T16:23:10.103277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da08cd2a-8c40-4569-9603-a9eeff179017 · inbound

Learning Fast, Tool aware Collision Avoidance for Collaborative Robots cites this paper.

Learning Fast, Tool aware Collision Avoidance for Collaborative Robots Learning to be Safe: Deep RL with a Safety Critic

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:28.891595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e12a26a-0b8f-4316-a43e-de9d1cb365ea · inbound

Approximating Safety Feedback Without a Safety Oracle via Model Predictive Control cites this paper.

Approximating Safety Feedback Without a Safety Oracle via Model Predictive Control Learning to be Safe: Deep RL with a Safety Critic

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T08:23:16.668291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3abba77-75ad-4b86-a67c-5753fb80edfa · inbound

Safe Continual Reinforcement Learning in Non-stationary Environments cites this paper.

Safe Continual Reinforcement Learning in Non-stationary Environments Learning to be Safe: Deep RL with a Safety Critic

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:43:24.798428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8901310b-6665-48de-a48d-8d1ba7f398e8 · inbound

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration cites this paper.

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration Learning to be Safe: Deep RL with a Safety Critic

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:57:29.832146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9de4f625-d4bd-4dae-b09b-d156228dca6c · inbound

UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning cites this paper.

UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning Learning to be Safe: Deep RL with a Safety Critic

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T10:58:02.719564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 47bd04e3-9676-486c-956f-e380a6bf15f0 · inbound

SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions cites this paper.

SafeExplorer: An Unbiased Policy Gradient for Reinforcement Learning with Recovery Interventions Learning to be Safe: Deep RL with a Safety Critic

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T05:46:03.704125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.