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Paper Citation Record · LEDGER

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games

As of 19 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 0 inbound Pith citation observations for arXiv:2509.03682.

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

pith.paper-citation-record.v1
2509.03682 v1

Coverage vector

measured 84 of 84 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:51:56.870832Z

measured 84 of 84 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

84 of 84 outbound references displayed

  • verified exact7
  • verified fuzzy51
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e3dc315-d850-4fc2-820d-63b97b8903ad · outbound

This paper cites Video games remain America’s favorite pastime with more than 212 million Americans playing regularly,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Video games remain America’s favorite pastime with more than 212 million Americans playing regularly,

Reference 1

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no resolver link, observed 2026-08-05T10:51:55.329704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 4959a035-d10b-4c98-aa5d-b0791b2980a5 · outbound

This paper cites GVR-4-68038-527-4,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games GVR-4-68038-527-4,

Reference 2

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no resolver link, observed 2026-08-05T10:51:55.451538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1a43f34c-6445-4bb8-8323-dd4baa3d197d · outbound

This paper cites 2024 essential facts about the U.S. video game industry,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games 2024 essential facts about the U.S. video game industry,

Reference 3

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no resolver link, observed 2026-08-05T10:51:55.630270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e821a649-c194-490d-9def-fdce0bc1d069 · outbound

This paper cites A machine for playing the game Nim,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A machine for playing the game Nim,

Reference 4

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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.

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Observation e781a5bc-48fa-45b4-a7dd-9281d36801f8 · outbound

This paper cites Understanding behavior trees,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Understanding behavior trees,

Reference 5

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verified fuzzy
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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.

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Observation f00c2ad3-b3e5-46ff-959d-47db68b687d8 · outbound

This paper cites an unresolved cited work.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-05T10:51:59.342462Z

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.

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Observation 876ef60d-4a86-4e90-a489-90a9932bb23f · outbound

This paper cites Using reinforcement learning to solve AI control problems,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Using reinforcement learning to solve AI control problems,

Reference 7

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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.

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Observation afddbaf4-788d-4db8-a08b-a3c70c22b08d · outbound

This paper cites Connectionist reinforcement learning for intelligent unit micromanagement in StarCraft,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Connectionist reinforcement learning for intelligent unit micromanagement in StarCraft,

Reference 8

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metadata mismatch
raw_fallback, observed 2026-08-05T10:51:58.901174Z

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.

source=pdf_text observed=2026-08-05T10:51:55.645303Z digest=sha256:3e16d731497d200bb6fb4a7f4aee83fa55505abd2f1ae7c1d65aef4e8fb2c824

Observation 6b3fadd6-b650-4553-8264-1c341bba557f · outbound

This paper cites Go with the flow: Reinforcement learning in turn-based battle video games,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Go with the flow: Reinforcement learning in turn-based battle video games,

Reference 9

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metadata mismatch
raw_fallback, observed 2026-08-05T10:51:58.824503Z

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.

source=pdf_text observed=2026-08-05T10:51:55.648388Z digest=sha256:721c7f95b9ac41b4a7bfcba267c971cbcad0c8dd64ecfe99703ff86be21a1315

Observation a134d1bf-93f5-4246-a593-2e5bd04fcecd · outbound

This paper cites A review of real -time strategy game AI,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A review of real -time strategy game AI,

Reference 10

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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.

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Observation 6979e9ac-d099-467c-8fc0-7f161b3142e1 · outbound

This paper cites Human -level control th rough deep reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Human -level control th rough deep reinforcement learning,

Reference 11

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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.

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Observation 24580eec-7934-4082-a9ce-fa355c2ea402 · outbound

This paper cites Playing FPS games with deep reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Playing FPS games with deep reinforcement learning,

Reference 12

Resolution
verified exact
doi, observed 2026-08-05T10:51:56.932869Z

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.

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Observation 42a572b5-4734-4521-aef1-a8900594b2fc · outbound

This paper cites Deep reinforcement learning for navigation in AAA video games,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Deep reinforcement learning for navigation in AAA video games,

Reference 13

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verified exact
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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.

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Observation a98459be-091b-4357-911d-0ce793105003 · outbound

This paper cites an unresolved cited work.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Unresolved cited work

Reference 14

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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.

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Observation 96f95acf-8b31-4010-9a16-ee6f6d426171 · outbound

This paper cites Mastering Atari, Go, chess and shogi by planning with a learned model,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Mastering Atari, Go, chess and shogi by planning with a learned model,

Reference 15

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verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.300124Z

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.

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Observation 1d45322b-cde5-4023-b260-d87baeef19e5 · outbound

This paper cites Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation

Reference 16

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local_arxiv, observed 2026-08-05T10:51:58.764087Z

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.

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Observation dd447118-6f53-4b10-91bd-8367c159e9cf · outbound

This paper cites Super - human performance in Gran Turismo Sport using deep reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Super - human performance in Gran Turismo Sport using deep reinforcement learning,

Reference 17

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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.

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Observation 818f4044-b49b-41ee-a22d-c328542eb0d3 · outbound

This paper cites Technical challenges of deploying reinforcement learning agents for game testing in AAA games,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Technical challenges of deploying reinforcement learning agents for game testing in AAA games,

Reference 18

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no resolver link, observed 2026-08-05T10:51:55.672432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 026565d2-c4ec-4647-bf27-6904c55f6a51 · outbound

This paper cites TD -Gammon, a self -teaching backgammon program, achieves master-level play,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games TD -Gammon, a self -teaching backgammon program, achieves master-level play,

Reference 19

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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.

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Observation dfd2c73a-3f25-4751-b5b4-afd271f86c32 · outbound

This paper cites Mastering the game of Go with dee p neural networks and tree search,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Mastering the game of Go with dee p neural networks and tree search,

Reference 20

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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.

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Observation eb4bf056-0c5d-49fd-8ebe-748b7f4ed752 · outbound

This paper cites Mastering the game of Go without human knowledge,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Mastering the game of Go without human knowledge,

Reference 21

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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.

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Observation 5b3b063c-cc52-466b-a99b-cd092439fefd · outbound

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

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A general reinforcement learning algorithm that masters chess, shogi, and Go through self -play,

Reference 22

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raw_fallback, observed 2026-08-05T10:51:59.257313Z

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.

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Observation 3f05b17c-0595-4caf-ac47-7fb4af6f2566 · outbound

This paper cites Grandmaster level in StarCraft II using multi -agent reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Grandmaster level in StarCraft II using multi -agent reinforcement learning,

Reference 23

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raw_fallback, observed 2026-08-05T10:51:59.248530Z

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.

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Observation 3950dc73-be06-4b29-a8fc-56182e26c553 · outbound

This paper cites Dota 2 with Large Scale Deep Reinforcement Learning.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Dota 2 with Large Scale Deep Reinforcement Learning

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.691234Z digest=sha256:a5c2ecff408fb60492a7d55e0120c63be17e13140032dee7173032f5789a08a0

Observation a5d431b0-ccab-4b04-a14e-1adfbdc53f0f · outbound

This paper cites A comprehensive survey of multiagent reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A comprehensive survey of multiagent reinforcement learning,

Reference 25

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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.

source=pdf_text observed=2026-08-05T10:51:55.694691Z digest=sha256:ad257d53b4215c8cefa3e149ad5c431c7e22fa51e8e43a747c146ec9641865b2

Observation 47c781be-8671-40e4-b91a-5110e6615ef6 · outbound

This paper cites Multi-agent reinforcement learning: A selective overview of theories and algorithms,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Multi-agent reinforcement learning: A selective overview of theories and algorithms,

Reference 26

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.697893Z digest=sha256:97d8dfd8910e29b0f1df187f0cc7fff885f3b7a996a21fff33fa92c9919c9b75

Observation b6ef752c-d339-4902-b255-d84b16bc6bcc · outbound

This paper cites Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,

Reference 27

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raw_fallback, observed 2026-08-05T10:51:59.240497Z

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.

source=pdf_text observed=2026-08-05T10:51:55.701361Z digest=sha256:372b207be3bcc70400b58aa547436391d03c4089a654d8ebdc455106796d9ae6

Observation 582067ed-b6a6-48c8-ad1d-849f26540707 · outbound

This paper cites Multi-agent reinforcement learning: A review of challenges and applications,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Multi-agent reinforcement learning: A review of challenges and applications,

Reference 28

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verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.231992Z

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.

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Observation fd6815f2-17ce-4627-9772-3248d1cb51fe · outbound

This paper cites Deep learning for video game playing,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Deep learning for video game playing,

Reference 29

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raw_fallback, observed 2026-08-05T10:51:59.223337Z

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.

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Observation f5e1955c-1ac4-4787-9075-f8c746809685 · outbound

This paper cites Reinforcement learning in game industry—Review, prospects and challenges,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Reinforcement learning in game industry—Review, prospects and challenges,

Reference 31

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raw_fallback, observed 2026-08-05T10:51:59.215027Z

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.

source=pdf_text observed=2026-08-05T10:51:55.717199Z digest=sha256:5a0b19c7972c06b6b33497466d58e212a15716e446f06c68d3a46152718dac63

Observation 320a8eb4-aeef-487f-8896-f017739cd99e · outbound

This paper cites Q-learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Q-learning,

Reference 32

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raw_fallback, observed 2026-08-05T10:51:59.206478Z

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.

source=pdf_text observed=2026-08-05T10:51:55.720267Z digest=sha256:65c6bc2e563b2087bd1851edc609d65847aea83f714b314aea27f0235ed7014d

Observation 54ac3f35-93ab-4148-a257-9fe103b35e6d · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Policy gradient methods for reinforcement learning with function approximation,

Reference 33

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raw_fallback, observed 2026-08-05T10:51:59.198183Z

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.

source=pdf_text observed=2026-08-05T10:51:55.723483Z digest=sha256:f32bc35a0f00b45de1ea8746e0a7663e12835dfe0510853e6b9f805272feaed8

Observation 7c7cb991-9f4c-41e6-922b-a6089de2e0f6 · outbound

This paper cites Wooldridge, An Introduction to MultiAgent Systems , 2nd ed.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Wooldridge, An Introduction to MultiAgent Systems , 2nd ed

Reference 34

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raw_fallback, observed 2026-08-05T10:51:59.189913Z

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.

source=pdf_text observed=2026-08-05T10:51:55.726589Z digest=sha256:b5234952a5d2396c3e8cec19404e12854e29af76600deae7580aa3bf876f3f4b

Observation 271dff9a-a9b8-4df8-9075-6fabca00527f · outbound

This paper cites Modular AI,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Modular AI,

Reference 35

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raw_fallback, observed 2026-08-05T10:51:59.181168Z

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.

source=pdf_text observed=2026-08-05T10:51:55.729621Z digest=sha256:28818141d38e58933a4967534cc813267137f0c0872f5aed87f2207f807d1ba3

Observation c5152184-8050-4e87-bb6f-292723c8c0d7 · outbound

This paper cites Long short -term memory,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Long short -term memory,

Reference 36

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raw_fallback, observed 2026-08-05T10:51:59.173091Z

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.

source=pdf_text observed=2026-08-05T10:51:55.732768Z digest=sha256:5a2a7e00e432e0dc3c6cd843b3c677625fe715a986df0766e303c0b52b666871

Observation 240dde50-bde1-4897-9eae-01f42ef520d2 · outbound

This paper cites Deep recurrent Q -learning for partially observable MDPs,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Deep recurrent Q -learning for partially observable MDPs,

Reference 37

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raw_fallback, observed 2026-08-05T10:51:59.165087Z

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.

source=pdf_text observed=2026-08-05T10:51:55.735651Z digest=sha256:e55ce1334866f77e0223a01e7a89d2791f50a1cf4346eeaf011fb2a5a5b65e1c

Observation 4185fa3e-7f39-4282-a920-c12c015e12ba · outbound

This paper cites Rainbow: Combining improvements in deep reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Rainbow: Combining improvements in deep reinforcement learning,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.156750Z

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.

source=pdf_text observed=2026-08-05T10:51:55.739171Z digest=sha256:38bdbafa11daf4ecaa891b3a2f9fe6fe68a181d69d84fea2a7872feb392f7150

Observation c0f59460-76f9-4e9d-ab05-a7c921e5764a · outbound

This paper cites Actor-critic algorithms,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Actor-critic algorithms,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.148062Z

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.

source=pdf_text observed=2026-08-05T10:51:55.742552Z digest=sha256:d2a3e73406e443b864b66f2ec640c8b8496dbf5608ac58de48b21af85864b776

Observation d5e8b191-0414-4978-b80f-ae085f5f78d0 · outbound

This paper cites Continuous control with deep reinforcement learning.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Continuous control with deep reinforcement learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.745729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.745729Z digest=sha256:981d113226a58bdd165bc377ffa9a1f937bd3878fe0a602d872e7f711be325a9

Observation e00150c8-e970-4b99-a887-2b0c88d917c7 · outbound

This paper cites Proximal Policy Optimization Algorithms.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Proximal Policy Optimization Algorithms

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.749220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.749220Z digest=sha256:dd7a730f460db0d54e0f466551d6d60212976a2c6708462a58c27453595bc071

Observation 3e8f741e-bf32-47b2-a334-188455af2349 · outbound

This paper cites Shoham and K.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Shoham and K

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.752466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.752466Z digest=sha256:26a1f8a15ab05589467cf4e22a5c5cd020400e075cfbd62e17a1325487ad379c

Observation 5c2b3359-e747-4b8b-a572-6b9c88c8d7b2 · outbound

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

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Markov games as a framework for multi -agent reinforcement learning,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.138716Z

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.

source=pdf_text observed=2026-08-05T10:51:55.755645Z digest=sha256:690bfc47a022660bf7122063dbbf53d26aeaa1c91aa955b8ab295de75f36b0b9

Observation 35e12052-9979-4661-b672-7cf6d0025978 · outbound

This paper cites Multiagent reinforcement learning: Theoretical framework and an algorithm,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Multiagent reinforcement learning: Theoretical framework and an algorithm,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.129920Z

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.

source=pdf_text observed=2026-08-05T10:51:55.759034Z digest=sha256:c5bc82be26dddd819b668bd61d75c5918d3f5bdc05fe234e7766ba4d0d4314a3

Observation 8902f8c8-294d-4d93-add5-e6776e5ee564 · outbound

This paper cites StarCraft II: A New Challenge for Reinforcement Learning.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games StarCraft II: A New Challenge for Reinforcement Learning

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.762086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.762086Z digest=sha256:b89b76bdd485c0d762265a3a8a925ceda68c2ed5b4d8b70a2606bd95c4d6d01b

Observation 5306b4e4-2b89-4618-b7ec-3ee871a64e7c · outbound

This paper cites The StarCraft Multi-Agent Challenge.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The StarCraft Multi-Agent Challenge

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.765630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.765630Z digest=sha256:5e66321221d868c84f7994f70b2c98620edc75e6289ef3d369e005a371712d8e

Observation fc3e5dbe-5ac3-47c7-acaf-b65725809964 · outbound

This paper cites Strangers and friends,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Strangers and friends,

Reference 47

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T10:51:58.549403Z

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.

source=pdf_text observed=2026-08-05T10:51:55.769658Z digest=sha256:7f41b769d91417315ad0d078fe66c9a8db7fcffc54ffc79ea69d8e6e706d3cbc

Observation c9fddd94-74a2-4742-9f6a-bbdb43bbfc84 · outbound

This paper cites Hierarchical macro strategy model for MOBA game AI,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Hierarchical macro strategy model for MOBA game AI,

Reference 48

Resolution
verified exact
doi, observed 2026-08-05T10:51:56.901678Z

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.

source=pdf_text observed=2026-08-05T10:51:55.773168Z digest=sha256:52af5b37a5e63595cb1fc0d77cc4bda243a157df613b1a307426388f956511d0

Observation b5b8daba-1c1d-48aa-9ce3-dcf29d763bbf · outbound

This paper cites Unifying temporal and structural credit assignment problems,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Unifying temporal and structural credit assignment problems,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.121596Z

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.

source=pdf_text observed=2026-08-05T10:51:55.776471Z digest=sha256:9e0a4b6e62314a9dd23156cf89331a2aa6d08665dc343159cc58f48fe0fe2fa1

Observation 082d136e-6184-4726-8ff8-1a16689a4d5b · outbound

This paper cites Multiagent systems: A survey from a machine learning perspective,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Multiagent systems: A survey from a machine learning perspective,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.112885Z

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.

source=pdf_text observed=2026-08-05T10:51:55.780653Z digest=sha256:31dc6eabb60db8483d6506cdb6084b4eef3cd19bf9e027bcfb6fbba6bf6da500

Observation b444575f-b6e4-4039-a178-70caab8228eb · outbound

This paper cites Temporal difference learning and TD-Gammon,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Temporal difference learning and TD-Gammon,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.104463Z

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.

source=pdf_text observed=2026-08-05T10:51:55.783776Z digest=sha256:73cafd18eef1a18e85b839a494c2a29491d862bb67ed8ece1f31cc5c593289b6

Observation 41f21917-81ed-417a-bbdb-01d46b2ab0d2 · outbound

This paper cites Creating pro-level AI for a real -time fighting game using deep reinfo rcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Creating pro-level AI for a real -time fighting game using deep reinfo rcement learning,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.095717Z

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.

source=pdf_text observed=2026-08-05T10:51:55.786788Z digest=sha256:0510a21c575e8380a65042d125cea9c500dcf8ee8abc02e99a90db9218cd1a48

Observation eeed4ad4-a83b-4536-8958-ff13cd1f0f2c · outbound

This paper cites On the utility of learning about humans for human-AI coordination,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games On the utility of learning about humans for human-AI coordination,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.086582Z

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.

source=pdf_text observed=2026-08-05T10:51:55.790699Z digest=sha256:78b1b1813b9b1f0c968b1c052070af62efdc3b9303bd6dde90ffa90fdea74b02

Observation c7166dbd-189a-4e17-a3c9-2a5a0656d18c · outbound

This paper cites IMPALA: Scalable distributed deep -RL with importance weighted actor -learner architectures,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games IMPALA: Scalable distributed deep -RL with importance weighted actor -learner architectures,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.077970Z

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.

source=pdf_text observed=2026-08-05T10:51:55.797249Z digest=sha256:bbd054bd3111efd31abda0c7517250c95b5c34e872fbcfba5e6e6c2f9cc57e09

Observation 553d7982-96b7-43f5-b711-3a185e24236b · outbound

This paper cites Distributed Prioritized Experience Replay.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Distributed Prioritized Experience Replay

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.800948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.800948Z digest=sha256:917aa309006d18230e5e6a8834a997ff769b8c9b425ef670c781a0be055c2bfa

Observation 5565a883-acd2-49b2-b9f5-2cbba4b28c59 · outbound

This paper cites Make a more engaging game w/ ML -Agents | Machine learning bots for game development | Reinforcement learning | Unity.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Make a more engaging game w/ ML -Agents | Machine learning bots for game development | Reinforcement learning | Unity

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.066823Z

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.

source=pdf_text observed=2026-08-05T10:51:55.804667Z digest=sha256:cd8a60581ab4113a70f24a58f3be03619cd6e0bc268b3fad6583cc7464808f92

Observation 054b1f78-c6ba-4b25-88e1-afee7779b33a · outbound

This paper cites Reinforcement Learning Agents for Ubisoft's Roller Champions.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Reinforcement Learning Agents for Ubisoft's Roller Champions

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:51:57.252051Z

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.

source=pdf_text observed=2026-08-05T10:51:55.807744Z digest=sha256:92002eec74ebebe4c69769cfb2156c731cb637c24b8b32a5eac841ed63bc2102

Observation edc2a936-49b7-4ec0-979d-91ac9037ea7b · outbound

This paper cites MonoBehaviour.FixedUpd ate,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games MonoBehaviour.FixedUpd ate,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.058040Z

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.

source=pdf_text observed=2026-08-05T10:51:55.811103Z digest=sha256:593ce9b988ed719872c89b30e64d73de3b647a2eea17c86a28a2bf4dbe030471

Observation df03024e-f9fc-4dd5-b893-03ac76d1ca89 · outbound

This paper cites On the verge of solving Rocket League using deep reinforcement learning and sim -to-sim transfer,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games On the verge of solving Rocket League using deep reinforcement learning and sim -to-sim transfer,

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.816478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.816478Z digest=sha256:de52f67ffe995d4821106200fa550bf93370c98af26935bb12fe183647de9bce

Observation ae116335-4acc-41bd-a285-294bdef4c91d · outbound

This paper cites On the potential of Rocket League for driving team AI development,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games On the potential of Rocket League for driving team AI development,

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.833322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.833322Z digest=sha256:6ee6173f2a538a93a1d4b125a2b60ee9e4a3d2420fdc1841e9b9289dc382d9fe

Observation 094bd1ae-03e0-4684-a67a-1824421a36c1 · outbound

This paper cites Lucy - SKG: Learning to play Rocket League efficiently using deep reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Lucy - SKG: Learning to play Rocket League efficiently using deep reinforcement learning,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.049561Z

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.

source=pdf_text observed=2026-08-05T10:51:55.954533Z digest=sha256:330ec6123f34c8464ea0ac91f06af1fa637399f6027be58719671422601501a9

Observation 9eb748f6-de58-4633-a415-2ce34a92f1e0 · outbound

This paper cites RLGym: Reinforcement learning in Rocket League,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games RLGym: Reinforcement learning in Rocket League,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.040763Z

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.

source=pdf_text observed=2026-08-05T10:51:56.026084Z digest=sha256:40edcd5730aa0b2e538dfda3b58f14eda33224231191678da345dddd2c733538

Observation 63952c26-80dc-4eae-b2dc-282e6a0bf633 · outbound

This paper cites Attention is all you need,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Attention is all you need,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.032720Z

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.

source=pdf_text observed=2026-08-05T10:51:56.079281Z digest=sha256:f3848b7bf96d138c4e696143e164519c36d5e09673d068ad4cb15dfc544cb9a2

Observation 5764aed7-f6c3-4485-8d58-538e0a4c93ee · outbound

This paper cites ViZDoom: A Doom-based AI research platform for visual reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games ViZDoom: A Doom-based AI research platform for visual reinforcement learning,

Reference 65

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T10:51:57.151109Z

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.

source=pdf_text observed=2026-08-05T10:51:56.136791Z digest=sha256:565c27ea93ead65a5c93ebad2d1f91884440c36363de15d7cca7dd61a5929762

Observation 19a7ccd9-0741-4dec-9c58-31546c90237f · outbound

This paper cites ViZDoom competitions: Playing Doom from pixels,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games ViZDoom competitions: Playing Doom from pixels,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.024639Z

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.

source=pdf_text observed=2026-08-05T10:51:56.164586Z digest=sha256:7314dd6b498bf10bb775bdf42cc203ff12cd8c003c336eea76783a5e04bbc8c9

Observation 5b694b81-2fec-4a90-aebe-9f8a40f199b9 · outbound

This paper cites Training agent for first -person shooter game with actor-critic curriculum learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Training agent for first -person shooter game with actor-critic curriculum learning,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.015956Z

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.

source=pdf_text observed=2026-08-05T10:51:56.181626Z digest=sha256:74390c52ea7ca7f4ac03c95b0f2e571d40a793170cb20bcae3f1b3334e5e4f95

Observation 248a5cbe-6cef-4ebd-8c7d-b93670f362d6 · outbound

This paper cites Asynchronous Methods for Deep Reinforcement Learning.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Asynchronous Methods for Deep Reinforcement Learning

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:56.247540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:56.247540Z digest=sha256:4abbb61b32524e230c3aacbef6faf4c0c4d26458b0777be8fafc61f517103d52

Observation 49c5c432-aa1e-4b8b-b95f-9775516cd58f · outbound

This paper cites A Mi necraft-based simulated task environment for human-AI teaming,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A Mi necraft-based simulated task environment for human-AI teaming,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:59.007758Z

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.

source=pdf_text observed=2026-08-05T10:51:56.364463Z digest=sha256:f13fa7b0047423f8897db835aeabe249575b6d1edafcfefc3ed490bc78b5d403

Observation 2b02d60e-b1cd-4ceb-afe1-53f4c027e04f · outbound

This paper cites Minecraft as an experimental world for AI in robotics,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Minecraft as an experimental world for AI in robotics,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.999155Z

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.

source=pdf_text observed=2026-08-05T10:51:56.478414Z digest=sha256:2118141a6549c671a228b5887c9ae586b0787b916903f4363a3d553db91f8045

Observation f5cf9446-1f41-4d73-a477-67dceeaac09b · outbound

This paper cites MineRL: A Large-Scale Dataset of Minecraft Demonstrations.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games MineRL: A Large-Scale Dataset of Minecraft Demonstrations

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:56.541888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:56.541888Z digest=sha256:5681e2b09f08a2ce88dce131004bffd2c69914611b1852465c957087d12ecd8e

Observation db2aee90-238f-43b6-830a-971aa0f9aade · outbound

This paper cites The Malmo platform for artificial intelligence experimentation,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The Malmo platform for artificial intelligence experimentation,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.990767Z

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.

source=pdf_text observed=2026-08-05T10:51:56.639919Z digest=sha256:1b66fae443e8c90c54d188a1e66953501765c4008f4cab96fe875703849833cc

Observation 01c0b6c7-1d91-4fe5-a6dd-ee4af71c91fc · outbound

This paper cites The Malmo Collaborative AI Challenge - Microsoft Research,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The Malmo Collaborative AI Challenge - Microsoft Research,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.982508Z

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.

source=pdf_text observed=2026-08-05T10:51:56.749020Z digest=sha256:8c6f639e1742ea144b910593befe51260391b19f284b49a664d76e252ae88237

Observation 7cec5d4a-6e77-4cf2-902d-0dc251050807 · outbound

This paper cites HogRider: Champion agent of Microsoft Malmo collaborative AI challenge,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games HogRider: Champion agent of Microsoft Malmo collaborative AI challenge,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.973783Z

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.

source=pdf_text observed=2026-08-05T10:51:56.839927Z digest=sha256:77eef034aa4c736673df55ffb7474328697b90deeddfec334ad63d0cae2f0454

Observation 7a5f6f14-9b39-45ac-ac7d-23ee78b5bfe8 · outbound

This paper cites The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) Competition.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The Multi-Agent Reinforcement Learning in Malm\"O (MARL\"O) Competition

Reference 75

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:51:56.968079Z

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.

source=pdf_text observed=2026-08-05T10:51:56.843854Z digest=sha256:a58414b56105b1418c839737cdc581f45bd6456b70bade08967e81ab33a0773c

Observation 196001a9-ce51-4e74-91b1-467615f4bc88 · outbound

This paper cites Human-level performance in 3D multiplayer games with population -based reinforcement learning,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Human-level performance in 3D multiplayer games with population -based reinforcement learning,

Reference 76

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.965344Z

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.

source=pdf_text observed=2026-08-05T10:51:56.846950Z digest=sha256:ac4d161c535610c728a6419d86285551d3a1fb20cd190d621ba304d64f2c62a1

Observation c3f383b5-3c07-40f8-ae77-efa9cff09a02 · outbound

This paper cites The exploration - exploitation dilemma: A multidisciplinary framework,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The exploration - exploitation dilemma: A multidisciplinary framework,

Reference 77

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.956756Z

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.

source=pdf_text observed=2026-08-05T10:51:56.849629Z digest=sha256:e60c09bf79d682666eca1f19ae739e9de86dd4c99badaef92b3c45e108ef1777

Observation f1e90818-862a-43aa-91f7-a7fc0841c3eb · outbound

This paper cites AlphaStar: Mastering the real -time strategy game StarCraft II,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games AlphaStar: Mastering the real -time strategy game StarCraft II,

Reference 78

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.947906Z

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.

source=pdf_text observed=2026-08-05T10:51:56.852283Z digest=sha256:b9e52de1b2f11accdeca6aa2dbf3fe8a9fb31b970788bf1f29a6bd99ae8f9493

Observation e5d1e515-c21c-4c48-9e89-6d7513a017fa · outbound

This paper cites Grid-wise control for multi-agent reinforcement learning in video game AI,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Grid-wise control for multi-agent reinforcement learning in video game AI,

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.938545Z

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.

source=pdf_text observed=2026-08-05T10:51:56.855374Z digest=sha256:0648dc1c30b4e7357005126a40ddb2f8d769c222d3dba669c7bbb25f861b4d4d

Observation 9df9b4dd-c55f-429e-87de-0df495dda9e0 · outbound

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

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:56.858825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:56.858825Z digest=sha256:29f6d86a430384403e661c4b5b5019af0e88368286a373ad6066c45d8d0eb5c2

Observation eff00d2b-5e4c-4e9d-9932-004e4866158b · outbound

This paper cites Ye, et al., Towards playing full MOBA games with deep reinforcement learning, vol.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Ye, et al., Towards playing full MOBA games with deep reinforcement learning, vol

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.928821Z

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.

source=pdf_text observed=2026-08-05T10:51:56.862563Z digest=sha256:37d4c6c62a746af5a354720f338c063109284e5aa6407ac97a41082172702f11

Observation 80e174de-bc66-4a8d-82ef-3ad4065713ba · outbound

This paper cites A recommender system for hero line-ups in MOBA games,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A recommender system for hero line-ups in MOBA games,

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.920163Z

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.

source=pdf_text observed=2026-08-05T10:51:56.865286Z digest=sha256:86cbe9f127f713e1344441ba3babca87e37f0a39d3448432276c68a8f406c6cb

Observation 7cf67657-3f80-4647-87b7-b2cd5872e63d · outbound

This paper cites The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games

Reference 83

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:51:56.945969Z

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.

source=pdf_text observed=2026-08-05T10:51:56.867925Z digest=sha256:e75d6b092186bab85b82ce0c3b38395f82917040037d1fcf491b3444609d06c7

Observation 36fbe881-e683-4567-876b-e6edf724df5f · outbound

This paper cites Acting with style: Towards designer-centred reinforcement learning for the video games industry,.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Acting with style: Towards designer-centred reinforcement learning for the video games industry,

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:51:58.911380Z

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.

source=pdf_text observed=2026-08-05T10:51:56.870832Z digest=sha256:281b99a1dc6b450e8965f1a53773445273882ba285e764d9566e49aefe2b506f

Observation 418233a4-e818-4275-857a-7746f2530211 · outbound

This paper cites A Survey of Deep Reinforcement Learning in Video Games.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games A Survey of Deep Reinforcement Learning in Video Games

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.714335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.714335Z digest=sha256:1fdce57b0635f736cbfd6bc51065be72d236fa1ec150fc4a72a454b21d19f985

Observation b57a8584-71b8-4c2a-97c9-eca9497c757d · outbound

This paper cites Available: https://www.grandviewresearch.com/ industry-analysis/video-game-market.

A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games Available: https://www.grandviewresearch.com/ industry-analysis/video-game-market

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-05T10:51:55.545689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:51:55.545689Z digest=sha256:5489a18c0b9d5247c48c879d68e04e91cea3922bc1c964ee5b92a00ed90bfdf1

Pith citing papers

No inbound Pith citation observations are available.