mlsanityData to 14 Oct 2026

Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning

arXiv 1902.03185 ↗First posted 8 Feb 2019cs.MA cs.AIcited by 20

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Abstract (arXiv)

Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards the importance of achieving an understanding of both an agent's internal design and external environment dynamics that facilitate cooperative behavior. In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Feb 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 5; 2024: 7; 2025: 1; 2026: 1.

20 papers.

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  1. 2024Moral Alignment for LLM Agentscited by 39
  2. 2021Cooperation and Reputation Dynamics with Reinforcement Learningcited by 13
  3. 2025Seven Security Challenges in Cross-domain Multi-agent LLM Systemscited by 12
  4. 2023Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learningcited by 10
  5. 2021Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RLcited by 7
  6. 2022The Importance of Credo in Multiagent Learningcited by 7
  7. 2023Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifestocited by 7
  8. 2022Exploring the Benefits of Teams in Multiagent Learningcited by 5
  9. 2023Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systemscited by 5
  10. 2021Statistical discrimination in learning agentscited by 4
  11. 2024Integrated Design and Governance of Agentic AI Systems through Adaptive Information Modulationcited by 4
  12. 2024TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learningcited by 3
8 more
  1. 2023Learning Roles with Emergent Social Value Orientationscited by 2
  2. 2023Steps Towards Satisficing Distributed Dynamic Team Trustcited by 1
  3. 2024Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agentscited by 1
  4. 2024Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learningcited by 1
  5. 2022Modelling the Recommender Alignment Problemcited by 0
  6. 2024FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Gamecited by 0
  7. 2024Evolution of Societies via Reinforcement Learningcited by 0
  8. 2026The Role of Network Topology and Opponent Information in Shaping Cooperation in Multi-Agent Reinforcement Learning Systemscited by 0