mlsanityData to 7 Oct 2026

An Algorithm For Adversary Aware Decentralized Networked MARL

arXiv 2305.05573 ↗First posted 9 May 2023cs.LG cs.MAcited by 0

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Abstract (arXiv)

Decentralized multi-agent reinforcement learning (MARL) algorithms have become popular in the literature since it allows heterogeneous agents to have their own reward functions as opposed to canonical multi-agent Markov Decision Process (MDP) settings which assume common reward functions over all agents. In this work, we follow the existing work on collaborative MARL where agents in a connected time varying network can exchange information among each other in order to reach a consensus. We introduce vulnerabilities in the consensus updates of existing MARL algorithms where agents can deviate from their usual consensus update, who we term as adversarial agents. We then proceed to provide an algorithm that allows non-adversarial agents to reach a consensus in the presence of adversaries under a constrained setting.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 9 May 2023.

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References · 17

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  3. 2019QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learningcited by 372
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  11. 2018Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPGcited by 20
  12. 2021MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelscited by 16
5 more
  1. 2018Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Ratescited by 13
  2. 2019Segregation Dynamics with Reinforcement Learning and Agent Based Modelingcited by 9
  3. 2013Modeling Human Decision-making in Generalized Gaussian Multi-armed Banditscited by 7
  4. 2017A Distributed Learning Dynamics in Social Groupscited by 3
  5. 2019Can social influence be exploited to compromise security: An online experimental evaluationcited by 1

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