mlsanityData to 14 Oct 2026

Collaboration Promotes Group Resilience in Multi-Agent RL

arXiv 2111.06614 ↗First posted 12 Nov 2021cs.LG cs.AI cs.MAcited by 2

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Abstract (arXiv)

To effectively operate in various dynamic scenarios, RL agents must be resilient to unexpected changes in their environment. Previous work on this form of resilience has focused on single-agent settings. In this work, we introduce and formalize a multi-agent variant of resilience, which we term group resilience. We further hypothesize that collaboration with other agents is key to achieving group resilience; collaborating agents adapt better to environmental perturbations in multi-agent reinforcement learning (MARL) settings. We test our hypothesis empirically by evaluating different collaboration protocols and examining their effect on group resilience. Our experiments show that all the examined collaborative approaches achieve higher group resilience than their non-collaborative counterparts.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 12 Nov 2021.

Builds on

14 of 30 references.

References · 14

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2015Prioritized Experience Replaycited by 1,099
  4. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 708
  5. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  6. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  7. 2017Multi-agent Reinforcement Learning in Sequential Social Dilemmascited by 209
  8. 2019MAVEN: Multi-Agent Variational Explorationcited by 185
  9. 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 154
  10. 2020Automatic Curriculum Learning For Deep RL: A Short Surveycited by 150
  11. 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 116
  12. 2020Robust Reinforcement Learning using Adversarial Populationscited by 66
2 more
  1. 2020A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environmentscited by 62
  2. 2020Parallel Knowledge Transfer in Multi-Agent Reinforcement Learningcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitationscited by 6
  2. 2026Learning Incentive Structures for Cooperative Resilience in Multi-Agent Systems under Social Dilemmascited by 0