# Enhancing Cooperation through Selective Interaction and Long-term Experiences in Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.02654 · HTML: https://mlsanity.com/paper/2405.02654/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 4 May 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [A multi-agent reinforcement learning model of common-pool resource appropriation](https://mlsanity.com/paper/1707.06600/) (2017)
- [Social diversity and social preferences in mixed-motive reinforcement learning](https://mlsanity.com/paper/2002.02325/) (2020)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings](https://mlsanity.com/paper/2106.09012/) (2021)
- [Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning](https://mlsanity.com/paper/1902.03185/) (2019)
- [Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2301.08491/) (2023)
- [Resolving social dilemmas with minimal reward transfer](https://mlsanity.com/paper/2310.12928/) (2023)
- [Knowing the past improves cooperation in the future](https://mlsanity.com/paper/1901.07493/) (2019)
- [Blocking defector invasion by focusing on the most successful partner](https://mlsanity.com/paper/2006.08560/) (2020)
- [Deconstructing Cooperation and Ostracism via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.04623/) (2023)

## Built on by (most cited first, 1 of 1)

- [ROSBag MCP Server: Analyzing Robot Data with LLMs for Agentic Embodied AI Applications](https://mlsanity.com/paper/2511.03497/) (2025)

## Abstract (arXiv)

The significance of network structures in promoting group cooperation within social dilemmas has been widely recognized. Prior studies attribute this facilitation to the assortment of strategies driven by spatial interactions. Although reinforcement learning has been employed to investigate the impact of dynamic interaction on the evolution of cooperation, there remains a lack of understanding about how agents develop neighbour selection behaviours and the formation of strategic assortment within an explicit interaction structure. To address this, our study introduces a computational framework based on multi-agent reinforcement learning in the spatial Prisoner's Dilemma game. This framework allows agents to select dilemma strategies and interacting neighbours based on their long-term experiences, differing from existing research that relies on preset social norms or external incentives. By modelling each agent using two distinct Q-networks, we disentangle the coevolutionary dynamics between cooperation and interaction. The results indicate that long-term experience enables agents to develop the ability to identify non-cooperative neighbours and exhibit a preference for interaction with cooperative ones. This emergent self-organizing behaviour leads to the clustering of agents with similar strategies, thereby increasing network reciprocity and enhancing group cooperation.