PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense | Paper Citation Record

Observation eb6c62a1-7c7f-45f1-bc72-a0109c6833fd · outbound

This paper cites Deep learning methods in network intrusion detection: Asurveyandanobjectivecomparison.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Deep learning methods in network intrusion detection: Asurveyandanobjectivecomparison

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:23.010882Z

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 661437bb-3d6f-437f-af1f-407c82504368 · outbound

This paper cites Unsupervised anomaly detection in network intrusion detection using clusters.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Unsupervised anomaly detection in network intrusion detection using clusters

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.999794Z

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 1f81c876-8036-4ebf-8da5-25bf409187d4 · outbound

This paper cites Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.987176Z

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 948bfdaf-b60f-47e2-970f-b763c9907512 · outbound

This paper cites Effectiveness of AI/ML in SOAR (Security Automation and Orchestration) Platforms.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Effectiveness of AI/ML in SOAR (Security Automation and Orchestration) Platforms

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.975406Z

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 69aab8fe-380e-4131-afc5-f1356a84eb0a · outbound

This paper cites Advancing cybersecurity: a comprehensive review of ai-driven detection techniques.Journal of Big Data, 11(1):105, 2024.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Advancing cybersecurity: a comprehensive review of ai-driven detection techniques.Journal of Big Data, 11(1):105, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.963211Z

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 2ca82047-e751-4fd1-86ed-14b255ea5707 · outbound

This paper cites Cyber-security and reinforcement learning — a brief survey.Engineering Applications of Artificial Intelligence, 114:105116, 2022.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Cyber-security and reinforcement learning — a brief survey.Engineering Applications of Artificial Intelligence, 114:105116, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.948499Z

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 c8aec987-f76c-44a2-a421-5fe7957577b7 · outbound

This paper cites Multi-agent reinforcement learning for cybersecurity: Classification and survey.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Multi-agent reinforcement learning for cybersecurity: Classification and survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.937189Z

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 b537314e-1c86-4d17-b1c1-8db871b71acb · outbound

This paper cites Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.073728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4cf91ab-27ec-4724-8611-fa71ea137a9d · outbound

This paper cites Finding the optimal security policies for autonomous cyber operations with competitive reinforcement learning.IEEE Access, 12:120292– 120305, 2024.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Finding the optimal security policies for autonomous cyber operations with competitive reinforcement learning.IEEE Access, 12:120292– 120305, 2024

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.925439Z

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 be1f6e09-5451-472d-aead-4cddd0ceb056 · outbound

This paper cites Deep Reinforcement Learning for Cyber Security.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Deep Reinforcement Learning for Cyber Security

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:57:22.606351Z

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 f8c2ac90-5c2d-4173-8dba-3a5c5f96f8dd · outbound

This paper cites Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.084932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09b794d4-8304-49d5-a308-cc6370ee3e8b · outbound

This paper cites CybORG: A Gym for the Development of Autonomous Cyber Agents.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense CybORG: A Gym for the Development of Autonomous Cyber Agents

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.913805Z

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 b6867f9f-c0cc-4a6c-bf9d-0742aabed150 · outbound

This paper cites CyberBattleSim - Microsoft Research.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense CyberBattleSim - Microsoft Research

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.902984Z

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 76fe6979-5651-4804-ae94-3595d54bcbfc · outbound

This paper cites an unresolved cited work.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:57:22.892103Z

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 1e98b636-d77f-4956-af47-0b8c8e3d18a5 · outbound

This paper cites FlipThem: Modeling Targeted Attacks with FlipIt for Multiple Resources.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense FlipThem: Modeling Targeted Attacks with FlipIt for Multiple Resources

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.881873Z

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 98032024-5bfd-47c1-a358-a10369506cc9 · outbound

This paper cites Are we compromised? modelling security assessment games.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Are we compromised? modelling security assessment games

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.870908Z

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 a1931fb6-5473-460c-a997-92f68685f54a · outbound

This paper cites QFlip: An adaptive reinforcement learning strategy for the FlipIt security game.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense QFlip: An adaptive reinforcement learning strategy for the FlipIt security game

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.859838Z

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 897c71c5-61ff-4009-8c01-7e8d42216818 · outbound

This paper cites Deep reinforcement learning for FlipIt security game.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Deep reinforcement learning for FlipIt security game

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.848234Z

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 61da93c8-de09-471c-b8f7-d2af2a1329d9 · outbound

This paper cites an unresolved cited work.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:57:22.836796Z

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 485115f7-d92d-4aab-9dea-799a01b8c1c5 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Playing Atari with Deep Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.119494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d7c7483-29aa-4c3e-92b4-63d3656a710a · outbound

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

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Grandmaster level in StarCraft II using multi-agent reinforcement learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.825740Z

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 a76a0665-a179-4c78-b4ac-45575d1e5d32 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.Nature, 529(7587):484–489, 2016.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Mastering the game of go with deep neural networks and tree search.Nature, 529(7587):484–489, 2016

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.814276Z

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 a5aa40d9-994c-4ebe-ab1d-32981acce99b · outbound

This paper cites Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.803010Z

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 15995327-f082-41ef-9fdc-bf93c70d9f55 · outbound

This paper cites A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.135024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73fa5e0a-fc6c-4c79-a2a5-077f48a82994 · outbound

This paper cites Proximal Policy Optimization Algorithms.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Proximal Policy Optimization Algorithms

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.139093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1c20764-4017-439d-9aeb-d3480df50f2d · outbound

This paper cites FlipNet: Modeling covert and persistent attacks on networked resources.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense FlipNet: Modeling covert and persistent attacks on networked resources

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.791843Z

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 bb8e29f8-d334-486f-b2d7-6be1f7716229 · outbound

This paper cites Flipleakage: A game-theoretic approach to protect against stealthy attackers in the presence of information leakage.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Flipleakage: A game-theoretic approach to protect against stealthy attackers in the presence of information leakage

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.779901Z

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 9bfbe2fd-ff73-40bc-a0d2-5678c5b69f56 · outbound

This paper cites Dynamic defense strategy against advanced persistent threat with insiders.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Dynamic defense strategy against advanced persistent threat with insiders

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.767225Z

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 b3eac515-10a6-447b-aa6e-0e0799941d00 · outbound

This paper cites Learning to Play against Any Mixture of Opponents.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Learning to Play against Any Mixture of Opponents

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:57:22.537337Z

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 4807c465-fb87-460c-aae9-d6312b406eea · outbound

This paper cites Human-level control through deep reinforcement learning.Nature, 518(7540):529–533, 2015.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Human-level control through deep reinforcement learning.Nature, 518(7540):529–533, 2015

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.756266Z

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 d8ff49bf-cc89-4d8f-9c0d-75fcac7a3c6f · outbound

This paper cites Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.163589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a19a9d1c-6678-4f93-ae52-6bb6933807f1 · outbound

This paper cites Oliehoek.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Oliehoek

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.743589Z

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 ab586ab0-acbb-4ba0-b210-93975e63b42a · outbound

This paper cites Comput- ing optimal equilibria and mechanisms via learning in zero-sum extensive-form games.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Comput- ing optimal equilibria and mechanisms via learning in zero-sum extensive-form games

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.728563Z

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 626fffac-5ce1-4f14-9c2a-6c7bb2275134 · outbound

This paper cites Deep reinforcement learning for green security games with real-time information.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Deep reinforcement learning for green security games with real-time information

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.715798Z

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 a37267d3-21b6-4662-8a56-48d2ed3e8e3b · outbound

This paper cites Robust Reinforcement Learning Under Minimax Regret for Green Security.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Robust Reinforcement Learning Under Minimax Regret for Green Security

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:57:22.507666Z

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 b9fd131d-86a4-4d67-9feb-18a44d69841c · outbound

This paper cites Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.182982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0765d367-6119-4a4f-bef3-19e4f4e142cf · outbound

This paper cites Patrol: Provable defense against adversarial policy in two-player games.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Patrol: Provable defense against adversarial policy in two-player games

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.704351Z

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 17364413-4dbe-46d3-b6bd-36a65432691e · outbound

This paper cites Efficient Policy Space Response Oracles.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Efficient Policy Space Response Oracles

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:57:22.479782Z

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 ddd816d4-27a6-41bb-bcd0-32cb3ee17da7 · outbound

This paper cites A survey on self-play methods in reinforcement learning, 2025.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense A survey on self-play methods in reinforcement learning, 2025

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.194157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a1e289-57cc-4cfb-9827-eb6e582ba73c · outbound

This paper cites Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.197956Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91fd7a50-09a1-4aa9-be09-6a1b988f0af4 · outbound

This paper cites Finding needles in a moving haystack: Prioritizing alerts with adversarial reinforcement learning.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Finding needles in a moving haystack: Prioritizing alerts with adversarial reinforcement learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.692781Z

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 6456b723-ef9a-498c-a2fa-77268abae9f8 · outbound

This paper cites Lee, Benjamin Lee, G.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Lee, Benjamin Lee, G

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.680222Z

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 41d3f7e4-6749-40b6-b7a5-43f593b76eb1 · outbound

This paper cites Oliehoek and Chris Amato.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Oliehoek and Chris Amato

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.668734Z

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 6d5de2a0-7586-4334-99ef-d77b1a93a2ec · outbound

This paper cites Scheduled Task/Job: Cron.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Scheduled Task/Job: Cron

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.655845Z

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 058dea0c-a5d6-4f60-aca0-59ea90033c6a · outbound

This paper cites On Autonomous Agents in a Cyber Defence Environment.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense On Autonomous Agents in a Cyber Defence Environment

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T16:57:22.217511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d0e3a9d-1e78-46f1-a5e2-24fb130ba057 · outbound

This paper cites Algorithmic game theory.Commun.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Algorithmic game theory.Commun

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.643177Z

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 2a08c994-ddcc-4e53-bbd7-5c6e4db52a62 · outbound

This paper cites Fusion-PSRO: Nash policy fusion for policy space response oracles, 2025.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Fusion-PSRO: Nash policy fusion for policy space response oracles, 2025

Reference 47

Resolution: verified exact; raw_fallback, observed 2026-08-15T16:57:22.378768Z

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 b39d9dbe-e212-4c0c-958e-62101545e1a2 · outbound

This paper cites Policy space diversity for non-transitive games.

PoolFlip: A Multi-Agent Reinforcement Learning Security Environment for Cyber Defense Policy space diversity for non-transitive games

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:57:22.630849Z

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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Reference resolution

Outbound references

Pith citing papers