Paper Citation Record · LEDGER

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach

As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.21397.

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

pith.paper-citation-record.v1
2507.21397 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:56:58.146019Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa43ecb4-40c0-46fe-9e3d-6329c4a2057b · outbound

This paper cites A practical guide to multi-objective reinforcement learning and planning,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach A practical guide to multi-objective reinforcement learning and planning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.585229Z

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Observation 2335be4c-0557-482b-9424-18431d3b1e57 · outbound

This paper cites Two-stage constrained actor-critic for short video recommendation,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Two-stage constrained actor-critic for short video recommendation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.574748Z

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Observation c54ef003-d5c1-40be-94fd-620c0c7df2b1 · outbound

This paper cites an unresolved cited work.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Unresolved cited work

Reference 3

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Observation a0ba49b2-07a2-4011-a9f2-01ce76611306 · outbound

This paper cites Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning

Reference 4

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Observation 8960fc44-6ba0-4421-8ed1-de03e4fee705 · outbound

This paper cites Recent theoretical advances in non-convex optimization,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Recent theoretical advances in non-convex optimization,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.557239Z

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Observation 5dde7745-e0af-4e66-85ac-9f1b645295c2 · outbound

This paper cites Federated multi- objective learning,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Federated multi- objective learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.548089Z

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Observation e20c2744-26a7-4b51-a809-cc4b285a74e6 · outbound

This paper cites Adaptive weighted sum method for multiobjective optimization: a new method for pareto front generation,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Adaptive weighted sum method for multiobjective optimization: a new method for pareto front generation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.538401Z

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Observation 77e9520b-053d-4597-9606-ee38f4a52d3a · outbound

This paper cites Multi-Objective LQR with Linear Scalarization.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Multi-Objective LQR with Linear Scalarization

Reference 8

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Observation d4c1cdcf-84a8-444d-851a-890cb9400575 · outbound

This paper cites Random hypervolume scalarizations for provable multi-objective black box optimization,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Random hypervolume scalarizations for provable multi-objective black box optimization,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.528494Z

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Observation d9031b6b-fd45-4be1-902a-25375671bd78 · outbound

This paper cites Multiple-gradient descent algorithm (mgda) for multi- objective optimization,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Multiple-gradient descent algorithm (mgda) for multi- objective optimization,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.519273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 85f6ac14-0a1f-471b-9c63-cb4199f74da3 · outbound

This paper cites Miettinen, Nonlinear multiobjective optimization.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Miettinen, Nonlinear multiobjective optimization

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.509473Z

Source-reported events for the cited work

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Observation 1e8e0052-7dc9-4624-bca4-c37aae547588 · outbound

This paper cites Complexity of gradient descent for multiobjective optimization,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Complexity of gradient descent for multiobjective optimization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.499944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e4f5d7f3-b0af-46d0-b4d9-7da84f84cfb1 · outbound

This paper cites The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach The stochastic multi-gradient algorithm for multi-objective optimization and its application to supervised machine learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.490024Z

Source-reported events for the cited work

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Observation c6ea9553-2fe2-459b-a7f3-998ef65bb746 · outbound

This paper cites Mitigating gradient bias in multi-objective learning: A provably convergent approach,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Mitigating gradient bias in multi-objective learning: A provably convergent approach,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.480273Z

Source-reported events for the cited work

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Observation d2a9d917-b656-46a7-8c0b-7a2790349017 · outbound

This paper cites Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Direction-oriented Multi-objective Learning: Simple and Provable Stochastic Algorithms

Reference 15

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Source-reported events for the cited work

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Observation dfaec3d1-35ca-4f9e-95cf-ab1a9993cf88 · outbound

This paper cites Multi-criteria reinforcement learning.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Multi-criteria reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.470294Z

Source-reported events for the cited work

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Observation 3deef8f7-69ec-4d7c-9c80-26ccb39754aa · outbound

This paper cites Reinforcement recommenda- tion with user multi-aspect preference,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Reinforcement recommenda- tion with user multi-aspect preference,

Reference 17

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Observation dffab944-2702-4da5-8c0d-0a00c4363fd9 · outbound

This paper cites Few for many: Tchebycheff set scalarization for many-objective optimization,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Few for many: Tchebycheff set scalarization for many-objective optimization,

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-06T12:56:58.292300Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c4c42fb9-8abf-4deb-8ea3-69f7e795ce73 · outbound

This paper cites A multi-objective/multi-task learning framework induced by pareto stationarity,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach A multi-objective/multi-task learning framework induced by pareto stationarity,

Reference 19

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51ab15c2-aa1a-4d51-9507-df65c499ee88 · outbound

This paper cites Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning

Reference 20

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Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Pareto multi- task learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.440002Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7fc45cfa-8481-4d88-b110-8b46b2dfa19c · outbound

This paper cites Multi-objective reinforcement learning for the expected utility of the return,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Multi-objective reinforcement learning for the expected utility of the return,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.430265Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d335029c-a979-4165-993c-6a91a6e97367 · outbound

This paper cites On finite-time convergence of actor-critic algorithm,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach On finite-time convergence of actor-critic algorithm,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.420122Z

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Observation 0f909eb7-c329-4fd6-ada5-b76e92e88cd3 · outbound

This paper cites Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms

Reference 24

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ac70b17-5ff8-4113-8eab-f5d8b5e15629 · outbound

This paper cites Finite-time convergence and sample complexity of multi-agent actor-critic reinforcement learning with aver- age reward,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Finite-time convergence and sample complexity of multi-agent actor-critic reinforcement learning with aver- age reward,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.409718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2d01debc-671d-4c8b-adc3-7035f0a2cc65 · outbound

This paper cites Fully decen- tralized multi-agent reinforcement learning with networked agents,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Fully decen- tralized multi-agent reinforcement learning with networked agents,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.399169Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3ec279f6-9ff0-4d5c-bf80-c36f6d37e54f · outbound

This paper cites Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bdca3921-7f6b-48be-b0b5-867a00e93398 · outbound

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Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Average cost temporal-difference learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.387720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation df7b27c1-a7c3-459e-9dd3-593f8d39e4d8 · outbound

This paper cites On the convergence of stochastic multi-objective gradient manipulation and beyond,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach On the convergence of stochastic multi-objective gradient manipulation and beyond,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.376462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 02f480a6-5ef9-436a-9739-53e2dc8d19b5 · outbound

This paper cites Multi-task learning as multi-objective optimization,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Multi-task learning as multi-objective optimization,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.366148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 41561cf4-884c-435d-8fe8-edd5dfbe512f · outbound

This paper cites Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-06T12:56:58.182805Z

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Observation 95be232f-11ec-4b4a-957f-f401898d6fe8 · outbound

This paper cites Direction-oriented multi-objective learning: Simple and provable stochastic algorithms,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Direction-oriented multi-objective learning: Simple and provable stochastic algorithms,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.355531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 60d6eb6f-060b-41fe-9663-5e76d6358fff · outbound

This paper cites Reinforcement learning to optimize long-term user engagement in recommender systems,.

Enabling Pareto-Stationarity Exploration in Multi-Objective Reinforcement Learning: A Multi-Objective Weighted-Chebyshev Actor-Critic Approach Reinforcement learning to optimize long-term user engagement in recommender systems,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T12:56:58.344937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

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