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

## TL;DR

This paper explores how partner selection mechanisms in multi-agent reinforcement learning can foster cooperation among agents with selfish objectives, leading to more prosocial behaviors.

## Contribution

It introduces a partner selection approach that encourages cooperation in agents trained with purely selfish goals, highlighting the role of dynamic interactions.

## Key findings

- Agents learn to retaliate against defectors.
- Partner selection promotes cooperation.
- Results show emergence of prosocial behavior.

## Abstract

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.