Paper Citation Record · LEDGER

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

As of 9 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 2 inbound Pith citation observations for arXiv:1906.11046.

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

pith.paper-citation-record.v1
1906.11046 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-25T16:45:19.122215Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:46:50.757117Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-06-28T20:32:37.495366Z

Reference resolution

25 of 25 outbound references displayed

verified exact4
verified fuzzy19
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e1900e86-67d7-4c31-9d7e-b019255746e6 · outbound

This paper cites write newline.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis write newline

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.589799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ff0cef64-952a-4a51-805a-b9305e2e320a · outbound

This paper cites and Chriss, N.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis and Chriss, N

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.641278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c084918c-3849-4d0f-a32f-0a1ed23579a5 · outbound

This paper cites Emergent Complexity via Multi-Agent Competition.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Emergent Complexity via Multi-Agent Competition

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-05-25T16:46:03.220006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0345d443-c4c5-4e48-9047-7238da26e438 · outbound

This paper cites A., Brennan, T., Korajczyk, R., Mcdonald, R., and Vissing-jorgensen, A.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis A., Brennan, T., Korajczyk, R., Mcdonald, R., and Vissing-jorgensen, A

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.604973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4ba17b36-646a-4072-87e5-2a129b5dbed6 · outbound

This paper cites Deep hedging.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Deep hedging

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.611679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7896c2e-e9c1-4af7-91c5-0a416de0cd48 · outbound

This paper cites H., Kohli, P., and Whiteson, S.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis H., Kohli, P., and Whiteson, S

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.658360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b7bb54c0-0e58-449f-b741-ceb5f42ed4ee · outbound

This paper cites High-frequency trading.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis High-frequency trading

Reference 7

Resolution: verified exact; doi, observed 2026-05-25T16:46:02.945055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b1ff7d7-70c2-4ebf-9ee6-26142662cf64 · outbound

This paper cites and Wilcox, D.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis and Wilcox, D

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.601152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 32028b9f-7da1-47cc-a2da-6b5e86277035 · outbound

This paper cites Risk management via anomaly circumvent: Mnemonic deep learning for midterm stock prediction.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Risk management via anomaly circumvent: Mnemonic deep learning for midterm stock prediction

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.653009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1631e8f5-d55a-475b-8de5-a9c3d99af5bf · outbound

This paper cites Optimistic bull or pessimistic bear: adaptive deep reinforcement learning for stock portfolio allocation.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Optimistic bull or pessimistic bear: adaptive deep reinforcement learning for stock portfolio allocation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.649648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation dfa24edf-d9d9-4e68-a864-aa3a61f49205 · outbound

This paper cites P., Hunt, J.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis P., Hunt, J

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.597706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39544b02-6f20-4249-983a-c500d33c0280 · outbound

This paper cites P., and Mordatch, I.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis P., and Mordatch, I

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.608427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 78f251ea-c428-4c19-bb5d-2fd0e9f33684 · outbound

This paper cites Human-level control through deep reinforcement learning.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Human-level control through deep reinforcement learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.633754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3fda40ad-aaea-4f0a-bb7f-55a7c4e2d7ba · outbound

This paper cites P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.614917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fe1e7b9b-4e27-4167-82db-93367fed8333 · outbound

This paper cites P., and Vian, J.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis P., and Vian, J

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.622371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 97c1fc0b-829d-46ff-a111-07d5b476cd5e · outbound

This paper cites Prioritized Experience Replay.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Prioritized Experience Replay

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-25T16:46:03.238613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa2f0f18-b4bd-473f-9aac-600db3b2c603 · outbound

This paper cites J., Guez, A., et al.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis J., Guez, A., et al

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.637179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2533a983-1ebc-4611-ab3b-5163bd00c6f5 · outbound

This paper cites an unresolved cited work.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-05-25T16:46:03.593942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 241aa314-a44a-4afd-8c6b-45449477da62 · outbound

This paper cites Multiagent cooperation and competition with deep reinforcement learning.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Multiagent cooperation and competition with deep reinforcement learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.662781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6b8383db-c92d-4455-b41e-2feea01670bd · outbound

This paper cites Deep reinforcement learning with double q-learning.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Deep reinforcement learning with double q-learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.630188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3e7a0430-6c71-4cfe-a6e5-1bced28bcbfc · outbound

This paper cites Sample Efficient Actor-Critic with Experience Replay.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Sample Efficient Actor-Critic with Experience Replay

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-25T16:46:03.246212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d61184e5-75dd-4b3f-a407-3035f2565893 · outbound

This paper cites Practical deep reinforcement learning approach for stock trading.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Practical deep reinforcement learning approach for stock trading

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.619035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5bcf29de-2d07-46b7-a1e6-0389f1a5d92a · outbound

This paper cites A practical machine learning approach for dynamic stock recommendation.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis A practical machine learning approach for dynamic stock recommendation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.667020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 271ba9c6-394b-49b9-8fee-bcd14b324a67 · outbound

This paper cites Mean field multi-agent reinforcement learning.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Mean field multi-agent reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-25T16:46:03.626218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 04c597c6-5889-48b2-8cb6-cdadfe1994e5 · outbound

This paper cites Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization.

Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

Reference 25

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T16:46:03.232841Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

Observation 75de212b-2add-47be-9bbc-9fac0a605654 · inbound

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief cites this paper.

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-06-28T20:32:37.496773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a629de3d-813f-4809-87a8-2afad9e924dc · inbound

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief cites this paper.

Regularized Offline Policy Optimization with Posterior Hybrid Bayesian Belief Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T12:46:50.757117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.