# Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teams

arXiv: https://arxiv.org/abs/2304.07337 · HTML: https://mlsanity.com/paper/2304.07337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 14 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [Social diversity and social preferences in mixed-motive reinforcement learning](https://mlsanity.com/paper/2002.02325/) (2020)
- [Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents](https://mlsanity.com/paper/1903.00784/) (2019)
- [Expected Value of Communication for Planning in Ad Hoc Teamwork](https://mlsanity.com/paper/2103.01171/) (2021)
- [D3C: Reducing the Price of Anarchy in Multi-Agent Learning](https://mlsanity.com/paper/2010.00575/) (2020)
- [The Importance of Credo in Multiagent Learning](https://mlsanity.com/paper/2204.07471/) (2022)
- [Value Function Decomposition for Iterative Design of Reinforcement Learning Agents](https://mlsanity.com/paper/2206.13901/) (2022)
- [Exploring the Benefits of Teams in Multiagent Learning](https://mlsanity.com/paper/2205.02328/) (2022)

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

- [Towards a Better Understanding of Learning with Multiagent Teams](https://mlsanity.com/paper/2306.16205/) (2023)

## Abstract (arXiv)

Mixed incentives among a population with multiagent teams has been shown to have advantages over a fully cooperative system; however, discovering the best mixture of incentives or team structure is a difficult and dynamic problem. We propose a framework where individual learning agents self-regulate their configuration of incentives through various parts of their reward function. This work extends previous work by giving agents the ability to dynamically update their group alignment during learning and by allowing teammates to have different group alignment. Our model builds on ideas from hierarchical reinforcement learning and meta-learning to learn the configuration of a reward function that supports the development of a behavioral policy. We provide preliminary results in a commonly studied multiagent environment and find that agents can achieve better global outcomes by self-tuning their respective group alignment parameters.