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

arXiv: https://arxiv.org/abs/1902.03185 · HTML: https://mlsanity.com/paper/1902.03185/

## Key facts

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

## Built on by (most cited first, 20 of 20)

- [Moral Alignment for LLM Agents](https://mlsanity.com/paper/2410.01639/) (2024)
- [Cooperation and Reputation Dynamics with Reinforcement Learning](https://mlsanity.com/paper/2102.07523/) (2021)
- [Seven Security Challenges in Cross-domain Multi-agent LLM Systems](https://mlsanity.com/paper/2505.23847/) (2025)
- [Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.08491/) (2023)
- [Birds of a Feather Flock Together: A Close Look at Cooperation Emergence via Multi-Agent RL](https://mlsanity.com/paper/2104.11455/) (2021)
- [The Importance of Credo in Multiagent Learning](https://mlsanity.com/paper/2204.07471/) (2022)
- [Hybrid Approaches for Moral Value Alignment in AI Agents: a Manifesto](https://mlsanity.com/paper/2312.01818/) (2023)
- [Exploring the Benefits of Teams in Multiagent Learning](https://mlsanity.com/paper/2205.02328/) (2022)
- [Investigating the Impact of Direct Punishment on the Emergence of Cooperation in Multi-Agent Reinforcement Learning Systems](https://mlsanity.com/paper/2301.08278/) (2023)
- [Statistical discrimination in learning agents](https://mlsanity.com/paper/2110.11404/) (2021)
- [Integrated Design and Governance of Agentic AI Systems through Adaptive Information Modulation](https://mlsanity.com/paper/2409.10372/) (2024)
- [TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning](https://mlsanity.com/paper/2401.11512/) (2024)
- [Learning Roles with Emergent Social Value Orientations](https://mlsanity.com/paper/2301.13812/) (2023)
- [Steps Towards Satisficing Distributed Dynamic Team Trust](https://mlsanity.com/paper/2309.05378/) (2023)
- [Dynamics of Moral Behavior in Heterogeneous Populations of Learning Agents](https://mlsanity.com/paper/2403.04202/) (2024)
- [Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.02654/) (2024)
- [Modelling the Recommender Alignment Problem](https://mlsanity.com/paper/2208.12299/) (2022)
- [FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game](https://mlsanity.com/paper/2402.00738/) (2024)
- [Evolution of Societies via Reinforcement Learning](https://mlsanity.com/paper/2410.17466/) (2024)
- [The Role of Network Topology and Opponent Information in Shaping Cooperation in Multi-Agent Reinforcement Learning Systems](https://mlsanity.com/paper/2608.28977/) (2026)

## 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.