# Voting-Based Multi-Agent Reinforcement Learning for Intelligent IoT

arXiv: https://arxiv.org/abs/1907.01385 · HTML: https://mlsanity.com/paper/1907.01385/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks](https://mlsanity.com/paper/2206.10870/) (2022)
- [Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming](https://mlsanity.com/paper/2110.12929/) (2021)
- [A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.15092/) (2021)
- [Emergent Dominance Hierarchies in Reinforcement Learning Agents](https://mlsanity.com/paper/2401.12258/) (2024)
- [Federated Multi-Level Optimization over Decentralized Networks](https://mlsanity.com/paper/2310.06217/) (2023)

## Abstract (arXiv)

The recent success of single-agent reinforcement learning (RL) in Internet of things (IoT) systems motivates the study of multi-agent reinforcement learning (MARL), which is more challenging but more useful in large-scale IoT. In this paper, we consider a voting-based MARL problem, in which the agents vote to make group decisions and the goal is to maximize the globally averaged returns. To this end, we formulate the MARL problem based on the linear programming form of the policy optimization problem and propose a distributed primal-dual algorithm to obtain the optimal solution. We also propose a voting mechanism through which the distributed learning achieves the same sublinear convergence rate as centralized learning. In other words, the distributed decision making does not slow down the process of achieving global consensus on optimality. Lastly, we verify the convergence of our proposed algorithm with numerical simulations and conduct case studies in practical multi-agent IoT systems.