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Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication

arXiv 2601.18419 ↗First posted 26 Jan 2026quant-ph cs.AI cs.LGcited by 0

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Abstract (arXiv)

Emergent cooperation in classical Multi-Agent Reinforcement Learning has gained significant attention, particularly in the context of Sequential Social Dilemmas (SSDs). While classical reinforcement learning approaches have demonstrated capability for emergent cooperation, research on extending these methods to Quantum Multi-Agent Reinforcement Learning remains limited, particularly through communication. In this paper, we apply communication approaches to quantum Q-Learning agents: the Mutual Acknowledgment Token Exchange (MATE) protocol, its extension Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange (MEDIATE), the peer rewarding mechanism Gifting, and Reinforced Inter-Agent Learning (RIAL). We evaluate these approaches in three SSDs: the Iterated Prisoner's Dilemma, Iterated Stag Hunt, and Iterated Game of Chicken. Our experimental results show that approaches using MATE with temporal-difference measure (MATEtextsubscriptTD), AutoMATE, MEDIATE-I, and MEDIATE-S achieved high cooperation levels across all dilemmas, demonstrating that communication is a viable mechanism for fostering emergent cooperation in Quantum Multi-Agent Reinforcement Learning.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 26 Jan 2026.

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References · 15

  1. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  2. 2016Quantum Machine Learningcited by 326
  3. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  4. 2017Learning with Opponent-Learning Awarenesscited by 222
  5. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 167
  6. 2019Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learningcited by 93
  7. 2021Parametrized quantum policies for reinforcement learningcited by 90
  8. 2020Reinforcement Learning with Quantum Variational Circuitscited by 77
  9. 2017A multi-agent reinforcement learning model of common-pool resource appropriationcited by 69
  10. 2020Learning to Incentivize Other Learning Agentscited by 57
  11. 2022Policy Gradients using Variational Quantum Circuitscited by 15
  12. 2024Hybrid quantum programming with PennyLane Lightning on HPC platformscited by 9
3 more
  1. 2024eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channelscited by 8
  2. 2023Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimizationcited by 5
  3. 2024MEDIATE: Mutually Endorsed Distributed Incentive Acknowledgment Token Exchangecited by 1

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  2. 2026Quantum Frog: Emergent Cooperation and Difficulty Scaling in a Quantized-Time Cooperative Game
  3. 2026Quantum Advantage in Multi Agent Reinforcement Learning
  4. 2026MADQRL: Distributed Quantum Reinforcement Learning Framework for Multi-Agent Environments
  5. 2026Communication Methods in Multi-Agent Reinforcement Learning
  6. 2025Trust-Based Social Learning for Communication (TSLEC) Protocol Evolution in Multi-Agent Reinforcement Learning
  7. 2024Strategizing against Q-learners: A Control-theoretical Approach
  8. 2026The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs