Paper Citation Record · LEDGER

Group-Agent Reinforcement Learning with Heterogeneous Agents

As of 12 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2501.11818.

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

pith.paper-citation-record.v1
2501.11818 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:54:15.234655Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

42 of 42 outbound references displayed

verified exact0
verified fuzzy24
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d595296c-277c-4c08-afcd-25cc24ae2f1d · outbound

This paper cites Averaged-dqn: Variance reduction and stabilization for deep reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Averaged-dqn: Variance reduction and stabilization for deep reinforcement learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.969401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 43195795-b596-49ed-9140-ff6393c7882c · outbound

This paper cites The arcade learning environment: An evaluation platform for general agents.

Group-Agent Reinforcement Learning with Heterogeneous Agents The arcade learning environment: An evaluation platform for general agents

Reference 2

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Source-reported events for the cited work

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Observation ee6d6feb-50b3-4911-9366-50b374d44232 · outbound

This paper cites Bagging predictors.

Group-Agent Reinforcement Learning with Heterogeneous Agents Bagging predictors

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.939736Z

Source-reported events for the cited work

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Observation b707fcc8-526d-4d26-917e-9bfb71b54486 · outbound

This paper cites Openai gym, 2016.

Group-Agent Reinforcement Learning with Heterogeneous Agents Openai gym, 2016

Reference 4

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Source-reported events for the cited work

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Observation 3bc65c9a-76bd-44ee-bba8-a9a87a2fa851 · outbound

This paper cites UCB Exploration via Q-Ensembles.

Group-Agent Reinforcement Learning with Heterogeneous Agents UCB Exploration via Q-Ensembles

Reference 5

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Source-reported events for the cited work

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Observation eb0b5f0d-a2ed-42bc-b898-ff5bf253ea23 · outbound

This paper cites Ensemble network architecture for deep reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Ensemble network architecture for deep reinforcement learning

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.909535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 82bb5e4d-71fe-4604-ba6d-915d8f8e0541 · outbound

This paper cites Shared experience actor-critic for multi-agent reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Shared experience actor-critic for multi-agent reinforcement learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.885021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1d699731-5b88-4391-846e-5b98ac3c1763 · outbound

This paper cites Off-policy actor-critic.

Group-Agent Reinforcement Learning with Heterogeneous Agents Off-policy actor-critic

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.866951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 14becfc5-eee5-493e-b41e-115e2554285b · outbound

This paper cites Openai baselines.

Group-Agent Reinforcement Learning with Heterogeneous Agents Openai baselines

Reference 9

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Observation 0286253b-91c1-4d0e-a464-5688661beb18 · outbound

This paper cites Ensembles for continuous actions in reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Ensembles for continuous actions in reinforcement learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.834345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4f4762f9-0b4d-46ca-8a8e-fbdc59f637de · outbound

This paper cites Ensemble methods for reinforcement learning with function approximation.

Group-Agent Reinforcement Learning with Heterogeneous Agents Ensemble methods for reinforcement learning with function approximation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.815147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2972a5a8-2a9c-432e-aeaf-d6faf51c2305 · outbound

This paper cites Neural network ensembles in reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Neural network ensembles in reinforcement learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.793680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 555d45d6-574d-4d47-904f-3a955b08c49c · outbound

This paper cites Selective neural network ensembles in reinforcement learning: taking the advantage of many agents.

Group-Agent Reinforcement Learning with Heterogeneous Agents Selective neural network ensembles in reinforcement learning: taking the advantage of many agents

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.773761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 090c9a3c-5be9-4c3a-ad25-ac9c008c7680 · outbound

This paper cites A short introduction to boosting.

Group-Agent Reinforcement Learning with Heterogeneous Agents A short introduction to boosting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.752921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 59fd98ba-7c9a-4e27-bb28-58a9f28cf4d9 · outbound

This paper cites Random decision forests.

Group-Agent Reinforcement Learning with Heterogeneous Agents Random decision forests

Reference 15

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Source-reported events for the cited work

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Observation b7b66beb-958d-47de-875f-938149b2dcaf · outbound

This paper cites Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.722281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1eb34c0a-bee9-4f0f-b98d-b81eb892f234 · outbound

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

Group-Agent Reinforcement Learning with Heterogeneous Agents Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25444412-39e7-423d-885a-62a80e201cd3 · outbound

This paper cites Maven: Multi-agent variational exploration.

Group-Agent Reinforcement Learning with Heterogeneous Agents Maven: Multi-agent variational exploration

Reference 18

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Source-reported events for the cited work

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Observation 8e3c511e-eebc-48f4-9043-b8037ae09128 · outbound

This paper cites Albert bandura's social learning theory.

Group-Agent Reinforcement Learning with Heterogeneous Agents Albert bandura's social learning theory

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.670740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 456a951c-a068-44b4-949e-c68f1487e35d · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Playing Atari with Deep Reinforcement Learning

Reference 20

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Source-reported events for the cited work

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Observation cd848281-db10-43ca-9afd-c39c8537c975 · outbound

This paper cites Asynchronous methods for deep reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Asynchronous methods for deep reinforcement learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.654761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b83a95b7-a211-4b2a-8cf6-4fe325aa7289 · outbound

This paper cites Safe and efficient off-policy reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Safe and efficient off-policy reinforcement learning

Reference 22

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Source-reported events for the cited work

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Observation 5aca90c9-08a5-420c-a45d-7034708e33fa · outbound

This paper cites Split feature space ensemble method using deep reinforcement learning for algorithmic trading.

Group-Agent Reinforcement Learning with Heterogeneous Agents Split feature space ensemble method using deep reinforcement learning for algorithmic trading

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.627854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6530f2e-fa8d-4947-9e89-a76c2a190a48 · outbound

This paper cites Deep exploration via bootstrapped dqn.

Group-Agent Reinforcement Learning with Heterogeneous Agents Deep exploration via bootstrapped dqn

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5255880e-2a30-41ca-bd3a-eb7f87f61577 · outbound

This paper cites Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games.

Group-Agent Reinforcement Learning with Heterogeneous Agents Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Reference 25

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Source-reported events for the cited work

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Observation 5c2248b4-76e2-49d5-aa46-426133c41cf5 · outbound

This paper cites Value propagation for decentralized networked deep multi-agent reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Value propagation for decentralized networked deep multi-agent reinforcement learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.592590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec593ee5-6ac1-4247-bcf2-256062858e31 · outbound

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

Group-Agent Reinforcement Learning with Heterogeneous Agents Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.570061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3ee1cce3-8c26-4560-bed4-64d6bd817061 · outbound

This paper cites Seerl: Sample efficient ensemble reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Seerl: Sample efficient ensemble reinforcement learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.552348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 237fb3b6-8f96-4119-a331-c8cd588a2b3a · outbound

This paper cites Mastering atari, go, chess and shogi by planning with a learned model.

Group-Agent Reinforcement Learning with Heterogeneous Agents Mastering atari, go, chess and shogi by planning with a learned model

Reference 29

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Source-reported events for the cited work

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Observation 113e25bb-c575-4a37-b0bf-61cf7d9a143d · outbound

This paper cites Proximal Policy Optimization Algorithms.

Group-Agent Reinforcement Learning with Heterogeneous Agents Proximal Policy Optimization Algorithms

Reference 30

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Source-reported events for the cited work

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Observation 1399ff49-cc67-4eb3-8cf8-04c0182f3027 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Group-Agent Reinforcement Learning with Heterogeneous Agents Mastering the game of go with deep neural networks and tree search

Reference 31

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3fa27d88-a114-4b10-ba7b-f4f56a85f5d4 · outbound

This paper cites Mastering the game of go without human knowledge.

Group-Agent Reinforcement Learning with Heterogeneous Agents Mastering the game of go without human knowledge

Reference 32

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb7ebe73-58a2-47e4-813b-c7c8550ccd37 · outbound

This paper cites A general reinforcement learning algorithm that masters chess, shogi, and go through self-play.

Group-Agent Reinforcement Learning with Heterogeneous Agents A general reinforcement learning algorithm that masters chess, shogi, and go through self-play

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51bb53a8-36a0-4127-b8e5-ee6413dabdab · outbound

This paper cites Pebl: Pessimistic ensembles for offline deep reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Pebl: Pessimistic ensembles for offline deep reinforcement learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.489338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 12daf5e4-7578-4648-987a-bf530f3f22a9 · outbound

This paper cites Learning multiagent communication with backpropagation.

Group-Agent Reinforcement Learning with Heterogeneous Agents Learning multiagent communication with backpropagation

Reference 35

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Source-reported events for the cited work

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Observation 2def070e-00c4-4d1c-9600-031cf11c2d00 · outbound

This paper cites Value-decomposition networks for cooperative multi-agent learning based on team reward.

Group-Agent Reinforcement Learning with Heterogeneous Agents Value-decomposition networks for cooperative multi-agent learning based on team reward

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.460279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 21b145dc-394a-4ca6-bdb2-bd4c239c35e4 · outbound

This paper cites Roma: multi-agent reinforcement learning with emergent roles.

Group-Agent Reinforcement Learning with Heterogeneous Agents Roma: multi-agent reinforcement learning with emergent roles

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.436989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a28a96d2-a4e3-44e4-88a6-dee6acd00930 · outbound

This paper cites Fcmnet: Full communication memory net for team-level cooperation in multi-agent systems.

Group-Agent Reinforcement Learning with Heterogeneous Agents Fcmnet: Full communication memory net for team-level cooperation in multi-agent systems

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.420576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 39711266-bde9-4978-af7b-1137f7331254 · outbound

This paper cites Sample efficient actor-critic with experience replay.

Group-Agent Reinforcement Learning with Heterogeneous Agents Sample efficient actor-critic with experience replay

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.404722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bd7b7e09-74b6-4935-9fb9-7ec03857bd59 · outbound

This paper cites Ensemble algorithms in reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Ensemble algorithms in reinforcement learning

Reference 40

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Source-reported events for the cited work

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Observation 4d437267-2f75-453d-807f-181ae26111a9 · outbound

This paper cites Group-agent reinforcement learning.

Group-Agent Reinforcement Learning with Heterogeneous Agents Group-agent reinforcement learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.377611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dee14331-9472-4490-8eba-2c7a3a56d683 · outbound

This paper cites Deep reinforcement learning for automated stock trading: An ensemble strategy.

Group-Agent Reinforcement Learning with Heterogeneous Agents Deep reinforcement learning for automated stock trading: An ensemble strategy

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:54:15.359213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.