mlsanityData to 14 Oct 2026

Exploring the Benefits of Teams in Multiagent Learning

arXiv 2205.02328 ↗First posted 4 May 2022cs.AIcited by 5

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Abstract (arXiv)

For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal. Multiagent teams are primarily studied when in conflict; however, organizational psychology (OP) highlights the benefits of teams among human populations for learning how to coordinate and cooperate. In this paper, we propose a new model of multiagent teams for reinforcement learning (RL) agents inspired by OP and early work on teams in artificial intelligence. We validate our model using complex social dilemmas that are popular in recent multiagent RL and find that agents divided into teams develop cooperative pro-social policies despite incentives to not cooperate. Furthermore, agents are better able to coordinate and learn emergent roles within their teams and achieve higher rewards compared to when the interests of all agents are aligned.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 4 May 2022.

Builds on

15 of 30 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2019Emergent Tool Use From Multi-Agent Autocurriculacited by 313
  3. 2018Human-level performance in first-person multiplayer games with population-based deep reinforcement learningcited by 264
  4. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
  5. 2018Inequity aversion improves cooperation in intertemporal social dilemmascited by 106
  6. 2020Social diversity and social preferences in mixed-motive reinforcement learningcited by 49
  7. 2018Evolving intrinsic motivations for altruistic behaviorcited by 30
  8. 2020Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferencescited by 21
  9. 2019Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learningcited by 20
  10. 2021Expected Value of Communication for Planning in Ad Hoc Teamworkcited by 19
  11. 2021Cooperation and Reputation Dynamics with Reinforcement Learningcited by 13
  12. 2020Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teamscited by 12
3 more
  1. 2022The Importance of Credo in Multiagent Learningcited by 7
  2. 2021Cooperative and Competitive Biases for Multi-Agent Reinforcement Learningcited by 5
  3. 2020Grounding Artificial Intelligence in the Origins of Human Behaviorcited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 1.

5 papers.

  1. 2024Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmascited by 12
  2. 2022DM2: Decentralized Multi-Agent Reinforcement Learning for Distribution Matchingcited by 5
  3. 2023Presenting Multiagent Challenges in Team Sports Analyticscited by 2
  4. 2023Towards a Better Understanding of Learning with Multiagent Teamscited by 2
  5. 2023Learning to Learn Group Alignment: A Self-Tuning Credo Framework with Multiagent Teamscited by 1