An Algorithm For Adversary Aware Decentralized Networked MARL

2023-05-09 · Soumajyoti Sarkar

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.

📄 PDF Abstract BibTeX arXiv:2305.05573

Code (0)

등록된 구현이 없습니다.

Tasks

Multi-agent Reinforcement Learning

Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents

2018-12-06 · Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang 외

Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challenging problem. In this work, we address t…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning

2025-09-20 · Wei Duan, Jie Lu, Junyu Xuan arxiv

In networked multi-agent reinforcement learning (Networked-MARL), decentralized agents must act under local observability and constrained communication over fixed physical graphs. Existing methods often assume static nei…

Multi-agent Reinforcement Learning

Multi-agent Reinforcement Learning for Networked System Control

2020-04-03 · ICLR 2020 1 · Tianshu Chu, Sandeep Chinchali, Sachin Katti

This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning

2023-11-07 · Yao Zhang, Zhiwen Yu, Jun Zhang, Liang Wang 외

This paper considers optimal traffic signal control in smart cities, which has been taken as a complex networked system control problem. Given the interacting dynamics among traffic lights and road networks, attaining co…

Graph Learning Multi-agent Reinforcement Learning reinforcement-learning Traffic Signal Control

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

2018-02-23 · ICML 2018 7 · Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang 외

We consider the problem of \emph{fully decentralized} multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network. Specifically, we assume that the reward …