# Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing

arXiv: https://arxiv.org/abs/2104.07539 · HTML: https://mlsanity.com/paper/2104.07539/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Speeding Up Distributed Machine Learning Using Codes](https://mlsanity.com/paper/1512.02673/) (2015)
- [Coded Computation over Heterogeneous Clusters](https://mlsanity.com/paper/1701.05973/) (2017)
- [A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications](https://mlsanity.com/paper/1703.10750/) (2017)
- [Vehicular Edge Computing and Networking: A Survey](https://mlsanity.com/paper/1908.06849/) (2019)
- [Dynamic Heterogeneity-Aware Coded Cooperative Computation at the Edge](https://mlsanity.com/paper/1801.04357/) (2018)
- [A locality-based approach for coded computation](https://mlsanity.com/paper/2002.02440/) (2020)
- [On Batch-Processing Based Coded Computing for Heterogeneous Distributed Computing Systems](https://mlsanity.com/paper/1912.12559/) (2019)

## Abstract (arXiv)

Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices. However, offloading a computation task from a mobile device to other mobile devices is a challenging task due to frequent topology changes and link failures because of node mobility, unstable and unknown communication environments, and the heterogeneous nature of these devices. To address these challenges, in this paper, we introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL), which has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances, consideration of node heterogeneity, and decentralized load allocation. Comprehensive simulation studies demonstrate that the proposed approach can outperform state-of-the-art distributed computing schemes.