Paper Citation Record · LEDGER

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.21841.

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

pith.paper-citation-record.v1
2505.21841 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:35:51.668145Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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verified fuzzy21
unresolved10
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External citation measurements

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Outbound references

Observation 505562c8-8319-48b4-a76b-f495dc7b1a99 · outbound

This paper cites write newline.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints write newline

Reference 1

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Observation 630fe61f-14f5-4025-aa51-0741d651fe4b · outbound

This paper cites Constrained policy optimization.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Constrained policy optimization

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:58.539697Z

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Observation 00939444-31d3-4e08-bcf8-9f10515a817e · outbound

This paper cites Constrained Markov decision processes, volume 7.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Constrained Markov decision processes, volume 7

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:58.227495Z

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Observation 702aad4e-aa41-47b2-bc61-d37059c9958e · outbound

This paper cites Near-optimal regret bounds for reinforcement learning.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Near-optimal regret bounds for reinforcement learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:58.051680Z

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Observation f732cb5a-352a-4cee-a1b6-46c437e7cb63 · outbound

This paper cites G., Osband, I., and Munos, R.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints G., Osband, I., and Munos, R

Reference 5

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Observation 11fef369-1567-4313-a78b-c6b810038091 · outbound

This paper cites S., Agarwal, M., Koppel, A., and Aggarwal, V.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints S., Agarwal, M., Koppel, A., and Aggarwal, V

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:57.781747Z

Source-reported events for the cited work

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Observation eff5fcfe-0b06-4347-bcf2-9688eb8b64b2 · outbound

This paper cites DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:53.527372Z

Source-reported events for the cited work

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Observation 3347a759-18a2-4458-a690-11666c403118 · outbound

This paper cites Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Finding the Stochastic Shortest Path with Low Regret: The Adversarial Cost and Unknown Transition Case

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:53.355079Z

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Observation 900a97dd-cfee-456f-953d-bbc0c71787bc · outbound

This paper cites Learning infinite-horizon average-reward markov decision process with constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Learning infinite-horizon average-reward markov decision process with constraints

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:57.621924Z

Source-reported events for the cited work

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Observation 06e5816d-1349-4dd4-b8ec-d67ed52b0e08 · outbound

This paper cites Risk-constrained reinforcement learning with percentile risk criteria.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Risk-constrained reinforcement learning with percentile risk criteria

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:57.335859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8fcd96a9-0e4c-43af-b02f-be102f098bb2 · outbound

This paper cites Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Unifying pac and regret: Uniform pac bounds for episodic reinforcement learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:57.018850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cdc0c94a-c330-4168-81ad-7bdc10a34556 · outbound

This paper cites Provably efficient safe exploration via primal-dual policy optimization.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Provably efficient safe exploration via primal-dual policy optimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:56.831806Z

Source-reported events for the cited work

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Observation 6e50368c-c30d-4027-b003-6cdfbcd64dc7 · outbound

This paper cites Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:53.098564Z

Source-reported events for the cited work

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Observation 9b1fab1b-2b67-43ce-a413-b9e645253a4c · outbound

This paper cites Exploration-Exploitation in Constrained MDPs.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Exploration-Exploitation in Constrained MDPs

Reference 14

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Source-reported events for the cited work

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Observation 3ad0cfde-e1bf-4a66-b104-c999d0353657 · outbound

This paper cites A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints A Best-of-Both-Worlds Algorithm for Constrained MDPs with Long-Term Constraints

Reference 15

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Source-reported events for the cited work

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Observation e064fe05-10de-4988-a9a1-0ebb44156973 · outbound

This paper cites Provably efficient model-free constrained rl with linear function approximation.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Provably efficient model-free constrained rl with linear function approximation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:56.615244Z

Source-reported events for the cited work

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Observation d06a5db2-2c72-4b9a-bcbc-5682e2b3ec77 · outbound

This paper cites Online convex optimization with hard constraints: Towards the best of two worlds and beyond.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Online convex optimization with hard constraints: Towards the best of two worlds and beyond

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:56.433971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55b6921b-dfe2-4b24-92c6-c7e51f6ea4aa · outbound

This paper cites Safe reinforcement learning on autonomous vehicles.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Safe reinforcement learning on autonomous vehicles

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:56.252065Z

Source-reported events for the cited work

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Observation cd4ebc55-86eb-4332-9128-e09e759b68b5 · outbound

This paper cites Learning adversarial markov decision processes with bandit feedback and unknown transition.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Learning adversarial markov decision processes with bandit feedback and unknown transition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:56.073085Z

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Observation 4cc7faf8-aad4-4a61-8a87-fc00818c6150 · outbound

This paper cites Asymptotically Optimal Information-Directed Sampling.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Asymptotically Optimal Information-Directed Sampling

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:52.598973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3152b411-b341-40c6-8c39-757ffd025609 · outbound

This paper cites A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees

Reference 21

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Observation a8af659f-5cf7-463d-b447-43f7d5501947 · outbound

This paper cites An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints

Reference 22

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Source-reported events for the cited work

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Observation 0ae5c9b7-2a55-4a3d-8f5c-2bddaf7c3ce1 · outbound

This paper cites Learning policies with zero or bounded constraint violation for constrained MDPs.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Learning policies with zero or bounded constraint violation for constrained MDPs

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:55.912890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 43ca9dfd-247a-45e0-8f8d-a91c9e1a2c08 · outbound

This paper cites Learning policies with zero or bounded constraint violation for constrained mdps.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Learning policies with zero or bounded constraint violation for constrained mdps

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:55.746482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 30023b5d-0b14-481a-8259-27aae1a15833 · outbound

This paper cites Policy optimization in adversarial mdps: Improved exploration via dilated bonuses.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Policy optimization in adversarial mdps: Improved exploration via dilated bonuses

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:55.501758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0a2135b-3cbe-424a-981b-d9ac16ab7d0a · outbound

This paper cites Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Cancellation-Free Regret Bounds for Lagrangian Approaches in Constrained Markov Decision Processes

Reference 26

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Source-reported events for the cited work

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Observation 132bc39d-c3d1-47da-bf21-fd3bedf6e30a · outbound

This paper cites Truly No-Regret Learning in Constrained MDPs.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Truly No-Regret Learning in Constrained MDPs

Reference 27

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Observation 4bd08f26-9eb0-45c3-84b4-49c23f9636e1 · outbound

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An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:55.253773Z

Source-reported events for the cited work

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Observation d496fcf0-1dd2-49f9-b471-fda868e43a9b · outbound

This paper cites Upper confidence primal-dual reinforcement learning for CMDP with adversarial loss.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Upper confidence primal-dual reinforcement learning for CMDP with adversarial loss

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:55.038809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d7336e1-2e04-4a89-9b5c-f897fe1b595d · outbound

This paper cites and Sridharan, K.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints and Sridharan, K

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:54.836763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5435887d-aee8-47e5-be4a-1626685ea8d8 · outbound

This paper cites Learning in Markov Decision Processes under Constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Learning in Markov Decision Processes under Constraints

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:52.311657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69f3302e-3541-4a29-b121-6ab7b2a4be2f · outbound

This paper cites Optimal Algorithms for Online Convex Optimization with Adversarial Constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Optimal Algorithms for Online Convex Optimization with Adversarial Constraints

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:35:52.145545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a182d344-fcc4-42ee-bf95-7cbc40d03d95 · outbound

This paper cites Learning Adversarial MDPs with Stochastic Hard Constraints.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Learning Adversarial MDPs with Stochastic Hard Constraints

Reference 34

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Source-reported events for the cited work

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Observation b8adca20-6eb5-4929-a697-dde12424916b · outbound

This paper cites Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization

Reference 35

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Source-reported events for the cited work

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Observation 18847069-32b1-4364-aee1-77751cd6bdd9 · outbound

This paper cites Triple-Q: a model-free algorithm for constrained reinforcement learning with sublinear regret and zero constraint violation.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Triple-Q: a model-free algorithm for constrained reinforcement learning with sublinear regret and zero constraint violation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:54.549056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ac158d50-b7f6-4f0d-928e-2102d5c876fb · outbound

This paper cites A provably-efficient model-free algorithm for infinite-horizon average-reward constrained markov decision processes.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints A provably-efficient model-free algorithm for infinite-horizon average-reward constrained markov decision processes

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:54.348961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 10b44870-fe67-4b09-aa82-f812732727e1 · outbound

This paper cites Provably efficient model-free algorithms for non-stationary CMDP s.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Provably efficient model-free algorithms for non-stationary CMDP s

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:54.171541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 531160d9-5088-45ee-a051-a9a4dcc157ab · outbound

This paper cites an unresolved cited work.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:35:53.960811Z

Source-reported events for the cited work

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Observation 42c9f1b7-449e-4905-b842-e43a45cd2e29 · outbound

This paper cites and Ugot, O.-A.

An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints and Ugot, O.-A

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:35:53.728331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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