Paper Citation Record · LEDGER

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition

As of 6 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2606.21085.

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

pith.paper-citation-record.v1
2606.21085 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T14:53:31.878138Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

36 of 36 outbound references displayed

verified exact4
verified fuzzy0
unresolved32
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5e56c8ae-9b97-47ef-a257-e3ab9e932fe3 · outbound

This paper cites Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-04T06:09:36.628201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4da55aa2-6ad8-4802-b7e0-98228f68d925 · outbound

This paper cites D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-04T06:09:36.630789Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cb33f670-bd87-434c-9cdb-ef1ee4dad3ec · outbound

This paper cites Accelerating online reinforcement learning with offline datasets,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Accelerating online reinforcement learning with offline datasets,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation a15bbbce-6ff8-4103-8f59-f4e651534e2f · outbound

This paper cites Offline reinforcement learning with implicit q-learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Offline reinforcement learning with implicit q-learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 6a7b4799-8310-4170-9c84-13c087906096 · outbound

This paper cites Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 6791f76b-ff93-446f-b8af-404759e7aa66 · outbound

This paper cites A minimalist approach to offline reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition A minimalist approach to offline reinforcement learning,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 6c84a435-1ae6-4e59-8284-b3a2895a5301 · outbound

This paper cites Policy expansion for bridging offline-to- online reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Policy expansion for bridging offline-to- online reinforcement learning,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 04a7f2e6-32f9-49cf-8bd8-b433c0661fb8 · outbound

This paper cites Bayesian design principles for offline-to-online reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Bayesian design principles for offline-to-online reinforcement learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 3fdf59f5-5d5f-4c84-ae03-3b28cc7a7343 · outbound

This paper cites FACMAC: Factored Multi-Agent Centralised Policy Gradients.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition FACMAC: Factored Multi-Agent Centralised Policy Gradients

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:09:36.633975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a793b8a-4899-4a34-9253-3efceb7105b2 · outbound

This paper cites Opride: Efficient offline preference-based reinforcement learning via in-dataset exploration,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Opride: Efficient offline preference-based reinforcement learning via in-dataset exploration,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 3454fff4-12e0-48ac-a4e9-41f5ff6020eb · outbound

This paper cites Batch policy learning under constraints,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Batch policy learning under constraints,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 18438a75-e91c-4842-8308-1f619c6451a9 · outbound

This paper cites Flow to control: Offline reinforcement learning with lossless primitive discovery,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Flow to control: Offline reinforcement learning with lossless primitive discovery,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 648d7c2e-9240-434f-b9df-8ba1dc4e955e · outbound

This paper cites Off-policy deep reinforcement learning without exploration,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Off-policy deep reinforcement learning without exploration,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 6993e16f-fcb4-4349-9f1d-5890129fb102 · outbound

This paper cites Stabilizing off-policy q-learning via bootstrapping error reduction,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Stabilizing off-policy q-learning via bootstrapping error reduction,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 7c801630-ff42-4b77-aeda-774471b68a88 · outbound

This paper cites Behavior regularized offline reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Behavior regularized offline reinforcement learning,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 87c45629-542b-458e-8fdc-8758e736f135 · outbound

This paper cites Critic regularized regression,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Critic regularized regression,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 03d9989d-ce4d-4349-812c-7da7d6a898ab · outbound

This paper cites Advantage-weighted regression: Simple and scalable offline reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Advantage-weighted regression: Simple and scalable offline reinforcement learning,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 9695e938-52a9-43c4-a9db-dde80651fe69 · outbound

This paper cites A survey on offline multi-agent reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition A survey on offline multi-agent reinforcement learning,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation ba2c7537-050c-4a56-92a0-ba15e1e27f03 · outbound

This paper cites Offline multi-agent reinforcement learning: Guidelines and benchmarks,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Offline multi-agent reinforcement learning: Guidelines and benchmarks,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 2acc15f1-db4f-47b1-8ccc-6aee027fe0ca · outbound

This paper cites Offline multi-agent reinforcement learning with knowledge distillation,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Offline multi-agent reinforcement learning with knowledge distillation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 762331be-8f84-425d-af83-eccf69c2c4ba · outbound

This paper cites Offline decentralized multi-agent reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Offline decentralized multi-agent reinforcement learning,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 426bc625-36cd-42cc-96b5-4446e1dae85f · outbound

This paper cites Globediff: State diffusion process for partial observability in multi-agent systems,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Globediff: State diffusion process for partial observability in multi-agent systems,

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:09:36.625245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9466d7ba-4cd8-44a4-ab55-7a09b5094875 · outbound

This paper cites Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 487d87dd-ba1a-4789-9a00-575b8473d661 · outbound

This paper cites Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 9a4cfc7e-10bb-4c23-b975-d17992854b68 · outbound

This paper cites Counterfactual conservative q learning for offline multi-agent reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Counterfactual conservative q learning for offline multi-agent reinforcement learning,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 68e56995-11cc-410d-aaeb-817cbc992a6b · outbound

This paper cites Offline multi-agent rein- forcement learning with implicit global-to-local value regularization,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Offline multi-agent rein- forcement learning with implicit global-to-local value regularization,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation d46a85c3-6b36-40cf-8976-2b4de62608dc · outbound

This paper cites Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation a11b9d84-2657-476f-a518-92bdf153ea89 · outbound

This paper cites Qplex: Duplex dueling multi-agent q-learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Qplex: Duplex dueling multi-agent q-learning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 1eb8af10-ba6d-463f-a3f2-bfcbee1efdb8 · outbound

This paper cites Cal-ql: Calibrated offline reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Cal-ql: Calibrated offline reinforcement learning,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 868ee271-a8e6-41f7-a3dc-027c8fb10325 · outbound

This paper cites Proto: Iterative policy regularized offline-to-online reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Proto: Iterative policy regularized offline-to-online reinforcement learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 8a982434-94a4-4bd5-b4be-d5fc93266f52 · outbound

This paper cites Efficient online reinforcement learning with offline data,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Efficient online reinforcement learning with offline data,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation 53038486-179f-4854-bc5c-385ff3cdb7d0 · outbound

This paper cites Trust region policy optimisation in multi-agent reinforcement learning,.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Trust region policy optimisation in multi-agent reinforcement learning,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Observation a0061c53-725e-4e4b-9428-c7d416096b13 · outbound

This paper cites First, we incorporate the standardized public offline datasets released by the OMIGA benchmark [26].

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition First, we incorporate the standardized public offline datasets released by the OMIGA benchmark [26]

Reference 33

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Observation 6ed56350-bd92-47a8-97ab-c33aafd14583 · outbound

This paper cites an unresolved cited work.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Unresolved cited work

Reference 34

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Observation 4e75f9f9-1697-48c1-b93a-ec860bb1549a · outbound

This paper cites an unresolved cited work.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition Unresolved cited work

Reference 35

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Observation 14a89a6a-86fd-4cdd-9998-99d699ab03af · outbound

This paper cites To establish a rigor- ous multi-agent comparison, we extend these algorithms to their multi-agent counterparts, denoted as PEX-MA, AW AC-MA, RLPD-MA, and PROTO-MA.

Sim2O: Efficient Offline-to-Online MARL via Joint Action Composition To establish a rigor- ous multi-agent comparison, we extend these algorithms to their multi-agent counterparts, denoted as PEX-MA, AW AC-MA, RLPD-MA, and PROTO-MA

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T14:53:31.878138Z

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Pith citing papers

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