On-Device Deep Reinforcement Learning for Decentralized Task Offloading Performance trade-offs in the training process
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
Allowing less capable devices to offload computational tasks to more powerful devices or servers enables the development of new applications that may not run correctly on the device itself. Deciding where and why to run each of those applications is a complex task. Therefore, different approaches have been adopted to make offloading decisions. In this work, we propose a decentralized Deep Reinforcement Learning (DRL) agent to address the selection of computing locations. Unlike most existing work, we analyze it in a real testbed composed of various edge devices running the agent to determine where to execute each task. These devices are connected to a Multi-Access Edge Computing (MEC) server and a Cloud server through 5G communications. We evaluate not only the agent's performance in meeting task requirements but also the implications of running this type of agent locally, assessing the trade-offs of training locally versus remotely in terms of latency and energy consumption.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 3 of its 30 references that are in the map. First posted on arXiv 7 Jan 2026.
Builds on
3 of 30 references.
References · 3
- 2022On-device Training: A First Overview on Existing Systemscited by 19
- 2023Security and Privacy for Low Power IoT Devices on 5G and Beyond Networks: Challenges and Future Directionscited by 2
- 2025Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networkscited by 1
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- 2025A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
- 2025Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
- 2026Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
- 2024To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
- 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach
- 2024Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges
- 2024Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing
- 2024PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning