# Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions

arXiv: https://arxiv.org/abs/2204.02267 · HTML: https://mlsanity.com/paper/2204.02267/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 64 references that are in the map.
- First posted on arXiv 5 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [Mean Field Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1802.05438/) (2018)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing](https://mlsanity.com/paper/1510.00888/) (2015)
- [Decentralized Computation Offloading Game For Mobile Cloud Computing](https://mlsanity.com/paper/1404.3200/) (2014)
- [Optimal Bidding in Repeated Wireless Spectrum Auctions with Budget Constraints](https://mlsanity.com/paper/1608.07357/) (2016)

## Built on by (most cited first, 4 of 4)

- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Fusing Multiple Algorithms for Heterogeneous Online Learning](https://mlsanity.com/paper/2312.05432/) (2023)
- [Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application](https://mlsanity.com/paper/2208.04237/) (2022)
- [Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning](https://mlsanity.com/paper/2403.08879/) (2024)

## Abstract (arXiv)

We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing between competition and cooperation. The mechanism provably has Nash equilibria with optimal resource allocation in the static case. For a dynamic environment, we propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information, and a reward signal that reduces information need to a great extent. Empirical results confirm that through learning, agents significantly improve both system and individual performance, e.g., 40% offloading failure rate reduction, 32% communication overhead reduction, up to 38% computation resource savings in low contention, 18% utilization increase with reduced load variation in high contention, and improvement in fairness. Results also confirm the algorithm's good convergence and generalization property in significantly different environments.