Paper Citation Record · LEDGER

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

As of 15 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:1908.02269.

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

pith.paper-citation-record.v1
1908.02269 v4

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:54:44.217833Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

verified exact0
verified fuzzy20
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 31f3c23a-40dd-458b-ad90-a2b0870a94d5 · outbound

This paper cites Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:43.953588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84970a71-c76f-4102-8a03-f77eca30e11e · outbound

This paper cites Layer Normalization.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Layer Normalization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:43.962010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01a9811d-c5d6-4227-aa08-c87f81b65cb1 · outbound

This paper cites The option-critic architecture.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning The option-critic architecture

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:45.063890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 52f83007-9283-4099-932a-3b46882bad39 · outbound

This paper cites Measuring collaborative emergent behavior in multi-agent reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Measuring collaborative emergent behavior in multi-agent reinforcement learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:45.041024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f7470be0-522b-41e2-8c3d-701365afbee3 · outbound

This paper cites Intrinsically motivated reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Intrinsically motivated reinforcement learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:45.003405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d39358ca-3cd2-4be1-bddb-c0069c2195fb · outbound

This paper cites Learning to communicate with deep multi-agent reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Learning to communicate with deep multi-agent reinforcement learning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:43.998984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bc1adb-21a0-4e6a-83a1-ab4f9f7070b4 · outbound

This paper cites Bayesian action decoder for deep multi-agent reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Bayesian action decoder for deep multi-agent reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.967010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1c6afe98-c908-4f7a-b777-9f1ce5a37824 · outbound

This paper cites Counterfactual multi-agent policy gradients.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Counterfactual multi-agent policy gradients

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.942622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a130423e-82d1-4f7a-b308-117e4156787b · outbound

This paper cites Gupta, Maxim Egorov, and Mykel J.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Gupta, Maxim Egorov, and Mykel J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.921896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 22556c48-a8d6-4b99-95ff-eafb6a13f8ba · outbound

This paper cites Opponent modeling in deep reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Opponent modeling in deep reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.900238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 129480d0-e810-4143-adda-f77976f36055 · outbound

This paper cites A Survey and Critique of Multiagent Deep Reinforcement Learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning A Survey and Critique of Multiagent Deep Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.049340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abd0ce00-f827-4af1-bc49-f704efc88603 · outbound

This paper cites an unresolved cited work.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-14T14:54:44.877925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 04cbe614-582f-4f98-bc8a-fa3eb88473ee · outbound

This paper cites A Deep Policy Inference Q-Network for Multi-Agent Systems.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning A Deep Policy Inference Q-Network for Multi-Agent Systems

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.066304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10cb208-fce8-49c0-831a-ae2a06cf8f8d · outbound

This paper cites Actor-attention-critic for multi-agent reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Actor-attention-critic for multi-agent reinforcement learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.857945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b989619-573b-485b-a6a0-5c5e368f7a9e · outbound

This paper cites Categorical reparametrization with gumble-softmax.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Categorical reparametrization with gumble-softmax

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.083614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da6d8119-f219-4ed5-9504-639efdc9fbbe · outbound

This paper cites Social inﬂuence as intrinsic motivation for multi-agent deep reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Social inﬂuence as intrinsic motivation for multi-agent deep reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.823208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1fc2de41-4159-4a4a-a534-53c3c608b89b · outbound

This paper cites Learning attentional communication for multi-agent cooperation.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Learning attentional communication for multi-agent cooperation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.801844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 42ebaf7e-bf16-4354-93a7-4a234ef18050 · outbound

This paper cites Reinforcement learning: A survey.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Reinforcement learning: A survey

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.777650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c93dc2f8-bc49-4efe-afde-4e7b6ccce3df · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Adam: A Method for Stochastic Optimization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.111235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16709973-0c26-4dc5-80e1-a0517f3bb0ad · outbound

This paper cites Google Research Football: A Novel Reinforcement Learning Environment.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Google Research Football: A Novel Reinforcement Learning Environment

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.120179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62416596-1eb8-4359-8097-1aa993049d47 · outbound

This paper cites Multi-Agent Cooperation and the Emergence of (Natural) Language.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Multi-Agent Cooperation and the Emergence of (Natural) Language

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.128708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e95b0af-57b3-4e7d-be15-dd3959467b37 · outbound

This paper cites Continuous control with deep reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Continuous control with deep reinforcement learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.135861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation befe7fff-7d03-473c-9326-e6b6a947ec6e · outbound

This paper cites Markov games as a framework for multi-agent reinforcement learning.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Markov games as a framework for multi-agent reinforcement learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.144177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f1afdca-1cc3-471a-93f9-dd20ffda956b · outbound

This paper cites Multi- agent actor-critic for mixed cooperative-competitive environments.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Multi- agent actor-critic for mixed cooperative-competitive environments

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.740842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6e379780-130d-461f-84e0-42e43404be60 · outbound

This paper cites Maven: Multi- agent variational exploration.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Maven: Multi- agent variational exploration

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.716233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1096e428-e54c-48df-b0e0-ae19377565ef · outbound

This paper cites Emergence of grounded compositional language in multi- agent populations.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Emergence of grounded compositional language in multi- agent populations

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.697519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ed03b82e-b625-43bb-91d0-25e61f5229d0 · outbound

This paper cites Rectiﬁed linear units improve restricted boltzmann machines.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Rectiﬁed linear units improve restricted boltzmann machines

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.672750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8e72d5c7-6e71-4109-a574-adae660e8898 · outbound

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

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.652083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0b089523-2274-4f6f-829a-0aaad85d73a8 · outbound

This paper cites Opponent modeling in real-time strategy games.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Opponent modeling in real-time strategy games

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.629619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8fdc0df3-96d1-4a4b-bee7-de972d2b003d · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Dropout: a simple way to prevent neural networks from overﬁtting

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-14T14:54:44.191177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e52cd374-21f5-4329-bc95-5d70881be3f3 · outbound

This paper cites Learning to share and hide intentions using information regularization.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning Learning to share and hide intentions using information regularization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.578301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0d2c43a1-ebe2-4d31-b065-4e220b25a4e7 · outbound

This paper cites On the theory of the brownian motion.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning On the theory of the brownian motion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.555735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 54851eca-7b0a-4646-859b-6c282148b864 · outbound

This paper cites MADDPG + policy mask.

Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning MADDPG + policy mask

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:54:44.533474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.