Paper Citation Record · LEDGER

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2505.08630.

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

pith.paper-citation-record.v1
2505.08630 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:52:33.530541Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

30 of 30 outbound references displayed

verified exact0
verified fuzzy26
unresolved4
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8d33b721-84bd-412a-8a1d-f662866ae52e · outbound

This paper cites Hindsight experience replay.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Hindsight experience replay

Reference 1

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Source-reported events for the cited work

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Observation 8c9e9d1e-5b66-4c77-9e42-c9aa25e6b46d · outbound

This paper cites MASER: multi-agent rein- forcement learning with subgoals generated from experi- ence replay buffer.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning MASER: multi-agent rein- forcement learning with subgoals generated from experi- ence replay buffer

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.787734Z

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Observation bf7b0812-4807-43e6-be3d-2f99bf960837 · outbound

This paper cites Fox: Formation-aware explo- ration in multi-agent reinforcement learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Fox: Formation-aware explo- ration in multi-agent reinforcement learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.776092Z

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Observation 7a296d3b-10bb-46df-bd0b-5d5c9e21465f · outbound

This paper cites Estimating mu- tual information.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Estimating mu- tual information

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.755474Z

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Observation 54b54c3c-8eea-42af-aa7d-d05a99853d2c · outbound

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

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Multi-agent actor-critic for mixed cooperative-competitive environ- ments

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.732260Z

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Observation ad13d294-46f9-4c79-b3cd-6651fa657bd4 · outbound

This paper cites Oliehoek and Christo- pher Amato.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Oliehoek and Christo- pher Amato

Reference 14

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Source-reported events for the cited work

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Observation 90f398cc-94ed-4c3e-b51e-ee4dcb24ec8e · outbound

This paper cites Foerster, and Shimon Whiteson.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Foerster, and Shimon Whiteson

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.699755Z

Source-reported events for the cited work

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Observation 43e5dcd1-eeca-4d97-87c7-f3f52c19b38d · outbound

This paper cites Changing the environment based on em- powerment as intrinsic motivation.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Changing the environment based on em- powerment as intrinsic motivation

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.690806Z

Source-reported events for the cited work

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Observation fc12bb86-cdf8-4737-99eb-d0717e102de0 · outbound

This paper cites Strehl and Michael L.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Strehl and Michael L

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.643027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 95951287-c944-4b7b-91de-606143ea3300 · outbound

This paper cites Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Sutton, Joseph Modayil, Michael Delp, Thomas Degris, Patrick M

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.633340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc417c25-ab75-481a-95bb-1ae0f538fd5c · outbound

This paper cites #exploration: A study of count-based exploration for deep reinforcement learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning #exploration: A study of count-based exploration for deep reinforcement learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.621384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d1ee24a8-e59d-4b98-91d2-5c6f475ae71d · outbound

This paper cites DOP: off-policy multi-agent decomposed policy gradients.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning DOP: off-policy multi-agent decomposed policy gradients

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.608616Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f2e8ed37-2a3b-4d49-94b7-ff21e90df10a · outbound

This paper cites Hierarchical multi- agent skill discovery.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Hierarchical multi- agent skill discovery

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.588763Z

Source-reported events for the cited work

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Observation 52432704-367b-4d2c-b71f-70ad5add46ca · outbound

This paper cites To- ward socially friendly autonomous driving using multi- agent deep reinforcement learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning To- ward socially friendly autonomous driving using multi- agent deep reinforcement learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.578968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bd48d811-0a9e-4bba-889e-d921efe53cc3 · outbound

This paper cites QTRAN: learn- ing to factorize with transformation for cooperative multi- agent reinforcement learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning QTRAN: learn- ing to factorize with transformation for cooperative multi- agent reinforcement learning

Reference 1959

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.651507Z

Source-reported events for the cited work

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Observation 6d0cf810-8b2e-4cab-a728-c8fc1ec4bada · outbound

This paper cites Autotelic agents with intrinsically motivated goal-conditioned reinforce- ment learning: A short survey.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Autotelic agents with intrinsically motivated goal-conditioned reinforce- ment learning: A short survey

Reference 2003

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Observation cc2d02e6-ffea-4f98-bff9-31678768c56c · outbound

This paper cites Taylor, Wenyuan Tao, and Zhen Wang.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Taylor, Wenyuan Tao, and Zhen Wang

Reference 2004

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.743438Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e0f9e724-bbfd-4370-8694-06bc4bf7432f · outbound

This paper cites Efficient planning for factored infinite-horizon dec-pomdps.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Efficient planning for factored infinite-horizon dec-pomdps

Reference 2008

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Observation b4199dbf-a63e-43ac-abaf-b87a4627bfe1 · outbound

This paper cites A Survey of Temporal Credit Assignment in Deep Reinforcement Learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning A Survey of Temporal Credit Assignment in Deep Reinforcement Learning

Reference 2011

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Observation 0d41d978-e118-4580-b4bf-e0e6d54dc0e5 · outbound

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Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Unresolved cited work

Reference 2014

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3710980e-c7ab-473f-baa2-96b0978be5ce · outbound

This paper cites Coding theorems for a discrete source with a fidelity criterion.IRE Nat.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Coding theorems for a discrete source with a fidelity criterion.IRE Nat

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.662061Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3314bfcd-4cbd-48c2-a886-1d663410bf2c · outbound

This paper cites Exploit- ing locality of interaction in factored dec-pomdps.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Exploit- ing locality of interaction in factored dec-pomdps

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.717154Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ce8dea01-4790-4a52-9475-8eb3653678e1 · outbound

This paper cites Parsing reward.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Parsing reward

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.836220Z

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Observation 77e69a23-7231-4d3f-b7b7-2a5e8ce06845 · outbound

This paper cites ALMA: hierarchical learning for composite multi- agent tasks.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning ALMA: hierarchical learning for composite multi- agent tasks

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.797863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 37a95d83-e900-4e1f-bf28-1387319f1dc3 · outbound

This paper cites Universal value function ap- proximators.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Universal value function ap- proximators

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.672616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation acc543df-97eb-44fb-a671-4144f2569ba9 · outbound

This paper cites Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.807607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 867f3fd1-1a03-427a-958d-a0970ae7871e · outbound

This paper cites CM3: coop- erative multi-goal multi-stage multi-agent reinforcement learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning CM3: coop- erative multi-goal multi-stage multi-agent reinforcement learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.599270Z

Source-reported events for the cited work

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Observation aece811e-bb72-446c-ae15-667ab0db607b · outbound

This paper cites An empowerment-based solution to robotic manipulation tasks with sparse rewards.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning An empowerment-based solution to robotic manipulation tasks with sparse rewards

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.817320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57df9bec-3c6f-4a60-bb62-06c076368d90 · outbound

This paper cites Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?

Reference 2023

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Source-reported events for the cited work

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Observation f24a31fb-8c09-4cb1-bc21-53a4affc4404 · outbound

This paper cites Multi- target pursuit by a decentralized heterogeneous UA V swarm using deep multi-agent reinforcement learning.

Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning Multi- target pursuit by a decentralized heterogeneous UA V swarm using deep multi-agent reinforcement learning

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:52:33.766162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.