Paper Citation Record · LEDGER

Counterfactual Explanations for Continuous Action Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2505.12701.

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

pith.paper-citation-record.v1
2505.12701 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:31:53.778696Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T08:46:26.659681Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact0
verified fuzzy16
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2056b1f8-c7ef-4656-b32f-e056f1e928d6 · outbound

This paper cites Explaining reinforcement learning agents through counterfactual action outcomes.

Counterfactual Explanations for Continuous Action Reinforcement Learning Explaining reinforcement learning agents through counterfactual action outcomes

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.113684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e041eefd-042a-4b2b-a81e-6e12eeaa2902 · outbound

This paper cites Explaining reinforcement learning policies through counterfactual trajectories.ICML 2021 Workshop on Human in the Loop Learning,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Explaining reinforcement learning policies through counterfactual trajectories.ICML 2021 Workshop on Human in the Loop Learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.065985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 892061fb-54b4-43c8-a077-45d5850623d6 · outbound

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

Counterfactual Explanations for Continuous Action Reinforcement Learning Addressing function approximation error in actor-critic methods

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:53.714364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fa107e6-abbc-4833-a24a-1446170b4676 · outbound

This paper cites Counterfactual explana- tions and how to find them: literature review and bench- marking.Data Mining and Knowledge Discovery, pages 1–55,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Counterfactual explana- tions and how to find them: literature review and bench- marking.Data Mining and Knowledge Discovery, pages 1–55,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.023150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5a9db9b5-e7c9-4d08-bd45-e9254648af92 · outbound

This paper cites Stable-baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Research, 22(268):1–8,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Stable-baselines3: Reliable reinforcement learning implementations.Journal of Machine Learning Research, 22(268):1–8,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:53.742555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29186504-fe83-403e-816a-3d309f8ffe76 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Counterfactual Explanations for Continuous Action Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:53.747418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c36a74c-656b-4434-8733-37dc49c32a70 · outbound

This paper cites Reinforcement learn- ing application in diabetes blood glucose control: A systematic review.Artificial intelligence in medicine, 104:101836,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Reinforcement learn- ing application in diabetes blood glucose control: A systematic review.Artificial intelligence in medicine, 104:101836,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.931552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 87739827-454f-408e-8779-28133021bbde · outbound

This paper cites Counterfactual explanations and algorithmic recourses for machine learning: A review.ACM Computing Surveys, 56(12):1–42,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Counterfactual explanations and algorithmic recourses for machine learning: A review.ACM Computing Surveys, 56(12):1–42,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.898063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ad20a29a-d5c5-4df9-82fd-a7638398a887 · outbound

This paper cites Counterfactual explanations without opening the black box: automated decisions and the gdpr.Harvard Journal of Law and Technology, 31(2),.

Counterfactual Explanations for Continuous Action Reinforcement Learning Counterfactual explanations without opening the black box: automated decisions and the gdpr.Harvard Journal of Law and Technology, 31(2),

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.881925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 38bf4f93-0570-4e52-9c1a-a94b9ad17189 · outbound

This paper cites Reinforcement learning in healthcare: A survey.ACM Computing Surveys (CSUR), 55(1):1–36,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Reinforcement learning in healthcare: A survey.ACM Computing Surveys (CSUR), 55(1):1–36,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.866016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9b5a68d1-103a-42f7-bedb-a224e544eb49 · outbound

This paper cites Basal glucose control in type 1 dia- betes using deep reinforcement learning: An in silico vali- dation.IEEE Journal of Biomedical and Health Informat- ics, 25(4):1223–1232, 2020.

Counterfactual Explanations for Continuous Action Reinforcement Learning Basal glucose control in type 1 dia- betes using deep reinforcement learning: An in silico vali- dation.IEEE Journal of Biomedical and Health Informat- ics, 25(4):1223–1232, 2020

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.850237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e8220266-f949-48ec-89c4-d59f72af5733 · outbound

This paper cites Explainable reinforcement learning: A survey and comparative review.ACM Com- puting Surveys,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Explainable reinforcement learning: A survey and comparative review.ACM Com- puting Surveys,

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.989727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5f38af96-fb01-4de4-95db-6a315109a618 · outbound

This paper cites Explain the explainer: Interpreting model-agnostic counterfactual explanations of a deep reinforcement learning agent.IEEE Transactions on Artificial Intelligence, 5(04):1443–1457,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Explain the explainer: Interpreting model-agnostic counterfactual explanations of a deep reinforcement learning agent.IEEE Transactions on Artificial Intelligence, 5(04):1443–1457,

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.082078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e27242f4-a439-4705-916c-c716742b9edf · outbound

This paper cites Deep reinforcement learning for robotics: A sur- vey of real-world successes.Annual Review of Control, Robotics, and Autonomous Systems, 8,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Deep reinforcement learning for robotics: A sur- vey of real-world successes.Annual Review of Control, Robotics, and Autonomous Systems, 8,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.947963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 44c6ec6f-ac3e-4d50-8ebb-e753f85cdf56 · outbound

This paper cites Redefining counterfactual explanations for rein- forcement learning: Overview, challenges and opportuni- ties.ACM Computing Surveys, 56(9):1–33,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Redefining counterfactual explanations for rein- forcement learning: Overview, challenges and opportuni- ties.ACM Computing Surveys, 56(9):1–33,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.039116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dbd48c97-e001-47bd-aade-bbb9091fd4b1 · outbound

This paper cites Counterfactual explanations in sequential de- cision making under uncertainty.Advances in Neural In- formation Processing Systems, 34:30127–30139,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Counterfactual explanations in sequential de- cision making under uncertainty.Advances in Neural In- formation Processing Systems, 34:30127–30139,

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.914372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e05b021f-118d-4455-86c7-18701a8ae02c · outbound

This paper cites OpenAI Gym.

Counterfactual Explanations for Continuous Action Reinforcement Learning OpenAI Gym

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T20:31:53.698736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f380110f-5bcf-4960-8315-a396905250dd · outbound

This paper cites The uva/padova type 1 diabetes simulator: new features.Journal of Diabetes Science and Technology, 8(1):26–34,.

Counterfactual Explanations for Continuous Action Reinforcement Learning The uva/padova type 1 diabetes simulator: new features.Journal of Diabetes Science and Technology, 8(1):26–34,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.005934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 055db9f5-53d0-4cf1-b21e-7f70af440693 · outbound

This paper cites Coun- terfactual state explanations for reinforcement learning agents via generative deep learning.Artificial Intelligence, 295:103455,.

Counterfactual Explanations for Continuous Action Reinforcement Learning Coun- terfactual state explanations for reinforcement learning agents via generative deep learning.Artificial Intelligence, 295:103455,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:53.974245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dea9fcda-283f-476c-b141-fdf11c32f349 · outbound

This paper cites Learning “what-if” expla- nations for sequential decision-making.

Counterfactual Explanations for Continuous Action Reinforcement Learning Learning “what-if” expla- nations for sequential decision-making

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:31:54.098141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 532d6a33-4a7a-46a5-a2c2-9d631e8354be · inbound

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments cites this paper.

Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments Counterfactual Explanations for Continuous Action Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T08:46:26.659681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.