# An Algorithm For Adversary Aware Decentralized Networked MARL

arXiv: https://arxiv.org/abs/2305.05573 · HTML: https://mlsanity.com/paper/2305.05573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 17 of 17)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1905.05408/) (2019)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1702.08887/) (2017)
- [Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.07169/) (2020)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.04402/) (2021)
- [Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward](https://mlsanity.com/paper/2006.06626/) (2020)
- [Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG](https://mlsanity.com/paper/1811.07029/) (2018)
- [MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world models](https://mlsanity.com/paper/2103.03662/) (2021)
- [Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates](https://mlsanity.com/paper/1810.07792/) (2018)
- [Segregation Dynamics with Reinforcement Learning and Agent Based Modeling](https://mlsanity.com/paper/1909.08711/) (2019)
- [Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits](https://mlsanity.com/paper/1307.6134/) (2013)
- [A Distributed Learning Dynamics in Social Groups](https://mlsanity.com/paper/1705.03414/) (2017)
- [Can social influence be exploited to compromise security: An online experimental evaluation](https://mlsanity.com/paper/1909.02872/) (2019)

## 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.