# Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing

arXiv: https://arxiv.org/abs/2004.02315 · HTML: https://mlsanity.com/paper/2004.02315/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [Efficiently Quantifying Individual Agent Importance in Cooperative MARL](https://mlsanity.com/paper/2312.08466/) (2023)
- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing](https://mlsanity.com/paper/2312.15313/) (2023)
- [Atmosphere: Context and situational-aware collaborative IoT architecture for edge-fog-cloud computing](https://mlsanity.com/paper/2401.14968/) (2024)
- [Improved Reinforcement Learning in Cooperative Multi-agent Environments Using Knowledge Transfer](https://mlsanity.com/paper/2107.09807/) (2021)
- [Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN](https://mlsanity.com/paper/2305.10440/) (2023)
- [How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2312.08463/) (2023)
- [Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT](https://mlsanity.com/paper/2107.00481/) (2021)
- [A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2202.10612/) (2022)
- [Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.18433/) (2025)
- [ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture](https://mlsanity.com/paper/2506.13935/) (2025)
- [Federated Multi-Agent Reinforcement Learning for Privacy-Preserving and Energy-Aware Resource Management in 6G Edge Networks](https://mlsanity.com/paper/2509.10163/) (2025)

## Abstract (arXiv)

To support popular Internet of Things (IoT) applications such as virtual reality, mobile games and wearable devices, edge computing provides a front-end distributed computing archetype of centralized cloud computing with low latency. However, it's challenging for end users to offload computation due to their massive requirements on spectrum and computation resources and frequent requests on Radio Access Technology (RAT). In this paper, we investigate computation offloading mechanism with resource allocation in IoT edge computing networks by formulating it as a stochastic game. Here, each end user is a learning agent observing its local environment to learn optimal decisions on either local computing or edge computing with the goal of minimizing long term system cost by choosing its transmit power level, RAT and sub-channel without knowing any information of the other end users. Therefore, a multi-agent reinforcement learning framework is developed to solve the stochastic game with a proposed independent learners based multi-agent Q-learning (IL-based MA-Q) algorithm. Simulations demonstrate that the proposed IL-based MA-Q algorithm is feasible to solve the formulated problem and is more energy efficient without extra cost on channel estimation at the centralized gateway compared to the other two benchmark algorithms.