# Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems

arXiv: https://arxiv.org/abs/2002.08567 · HTML: https://mlsanity.com/paper/2002.08567/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities](https://mlsanity.com/paper/2305.17568/) (2023)
- [Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2405.16854/) (2024)
- [An Explainable Artificial Intelligence Framework for Quality-Aware IoE Service Delivery](https://mlsanity.com/paper/2201.10822/) (2022)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging](https://mlsanity.com/paper/2108.01466/) (2021)
- [Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid](https://mlsanity.com/paper/2306.07993/) (2023)
- [Deep Reinforcement Learning for Traveling Purchaser Problems](https://mlsanity.com/paper/2404.02476/) (2024)
- [QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2311.02525/) (2023)
- [Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation](https://mlsanity.com/paper/2506.16718/) (2025)

## Abstract (arXiv)

The stringent requirements of mobile edge computing (MEC) applications and functions fathom the high capacity and dense deployment of MEC hosts to the upcoming wireless networks. However, operating such high capacity MEC hosts can significantly increase energy consumption. Thus, a base station (BS) unit can act as a self-powered BS. In this paper, an effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. First, a two-stage linear stochastic programming problem is formulated with the goal of minimizing the total energy consumption cost of the system while fulfilling the energy demand. Second, a semi-distributed data-driven solution is proposed by developing a novel multi-agent meta-reinforcement learning (MAMRL) framework to solve the formulated problem. In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent. Sequentially, the meta-agent optimizes (i.e., exploits) the energy dispatch decision by accepting only the observations from each local agent with its own state information. Meanwhile, each BS agent estimates its own energy dispatch policy by applying the learned parameters from meta-agent. Finally, the proposed MAMRL framework is benchmarked by analyzing deterministic, asymmetric, and stochastic environments in terms of non-renewable energy usages, energy cost, and accuracy. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost (with 95.8% prediction accuracy), compared to other baseline methods.