Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks

• URL: http://arxiv.org/abs/2509.01257v2
• Date: Thu, 23 Oct 2025 10:39:25 GMT
• Title: Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks
• Authors: Andrea Fox, Francesco De Pellegrini, Eitan Altman,
• Abstract summary: In edge computing systems, autonomous agents must make fast local decisions while competing for shared resources.<n>We propose a decentralized framework in which each agent solves a constrained Markov decision process (CMDP), coordinating implicitly through a shared constraint vector.<n>Using safe reinforcement learning, agents learn policies that meet both local and global goals.
• Score: 3.065057842316041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In edge computing systems, autonomous agents must make fast local decisions while competing for shared resources. Existing MARL methods often resume to centralized critics or frequent communication, which fail under limited observability and communication constraints. We propose a decentralized framework in which each agent solves a constrained Markov decision process (CMDP), coordinating implicitly through a shared constraint vector. For the specific case of offloading, e.g., constraints prevent overloading shared server resources. Coordination constraints are updated infrequently and act as a lightweight coordination mechanism. They enable agents to align with global resource usage objectives but require little direct communication. Using safe reinforcement learning, agents learn policies that meet both local and global goals. We establish theoretical guarantees under mild assumptions and validate our approach experimentally, showing improved performance over centralized and independent baselines, especially in large-scale settings.

Related papers

• Multi-Agent Deep Reinforcement Learning Under Constrained Communications [2.7126292487109005]
  We present a distributed multi-agent reinforcement learning (MARL) framework that removes the need for centralized critics or global information.<n>We develop a novel Graph Attention Network (D-GAT) that performs global state inference through multi-hop communication.<n>We also develop the distributed graph-attention MAPPO (DG-MAPPO) -- a distributed MARL framework where agents optimize local policies and value functions.
  arXiv  Detail & Related papers  (2026-01-22T21:07:18Z)
• Multi-Agent Deep Reinforcement Learning for Collaborative UAV Relay Networks under Jamming Atatcks [36.380478794869234]
  This paper formulates this challenge as a cooperative Multi-Agent Reinforcement Learning (MARL) problem, solved using the Training with Decentralized Execution (CTDE) framework.<n>Our proposed framework significantly outperforms baselines, increasing total system throughput by approximately 50% while simultaneously achieving a near-zero collision rate.<n>A key finding is that the agents develop an emergent anti-jamming strategy without explicit programming.
  arXiv  Detail & Related papers  (2025-12-09T08:11:21Z)
• Scalable Multi-Agent Path Finding using Collision-Aware Dynamic Alert Mask and a Hybrid Execution Strategy [14.091856940824258]
  Multi-agent pathfinding is a critical problem in robotics and autonomous systems.<n>We propose a hybrid framework that combines decentralized path planning with a lightweight centralized coordinator.
  arXiv  Detail & Related papers  (2025-10-10T15:25:40Z)
• Diagnose, Localize, Align: A Full-Stack Framework for Reliable LLM Multi-Agent Systems under Instruction Conflicts [75.20929587906228]
  Large Language Model (LLM)-powered multi-agent systems (MAS) have rapidly advanced collaborative reasoning, tool use, and role-specialized coordination in complex tasks.<n>However, reliability-critical deployment remains hindered by a systemic failure mode: hierarchical compliance under instruction conflicts.
  arXiv  Detail & Related papers  (2025-09-27T08:43:34Z)
• Benchmarking LLMs' Swarm intelligence [50.544186914115045]
  Large Language Models (LLMs) show potential for complex reasoning, yet their capacity for emergent coordination in Multi-Agent Systems (MAS) remains largely unexplored.<n>We introduce SwarmBench, a novel benchmark designed to systematically evaluate tasks of LLMs acting as decentralized agents.<n>We propose metrics for coordination effectiveness and analyze emergent group dynamics.
  arXiv  Detail & Related papers  (2025-05-07T12:32:01Z)
• AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems [22.291969093748005]
  AgentNet is a decentralized, Retrieval-Augmented Generation (RAG)-based framework for multi-agent systems.<n>Unlike prior approaches with static roles or centralized control, AgentNet allows agents to adjust connectivity and route tasks based on local expertise and context.<n>Experiments show that AgentNet achieves higher task accuracy than both single-agent and centralized multi-agent baselines.
  arXiv  Detail & Related papers  (2025-04-01T09:45:25Z)
• DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints [1.1549572298362787]
  We propose a momentum-based decentralized gradient policy method, DePAint, to solve the problem. This is the first privacy-preserving fully decentralized multi-agent reinforcement learning algorithm that considers both peak and average constraints.
  arXiv  Detail & Related papers  (2023-10-22T16:36:03Z)
• Distributed Online Rollout for Multivehicle Routing in Unmapped Environments [0.8437187555622164]
  We present a fully distributed, online, and scalable reinforcement learning algorithm for the well-known multivehicle routing problem. Agents self-organize into local clusters and independently apply a multiagent rollout scheme locally to each cluster. Our algorithm achieves approximately a factor of two cost improvement over the base policy for a range of radii bounded from below and above by two and three times the critical sensing radius, respectively.
  arXiv  Detail & Related papers  (2023-05-24T22:06:44Z)
• Distributed Adaptive Learning Under Communication Constraints [54.22472738551687]
  This work examines adaptive distributed learning strategies designed to operate under communication constraints. We consider a network of agents that must solve an online optimization problem from continual observation of streaming data.
  arXiv  Detail & Related papers  (2021-12-03T19:23:48Z)
• Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance [5.37556626581816]
  Multi-agent reinforcement learning (MARL) has made remarkable advances in solving cooperative residential load scheduling problems.<n> centralized training, the most common paradigm for MARL, limits large-scale deployment in communication-constrained cloud-edge environments.<n>Our proposed approach is based on actor-critic methods, where the global critic is a learned function of individual critics computed solely based on local observations of households.
  arXiv  Detail & Related papers  (2021-10-06T14:05:26Z)
• Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning [52.7873574425376]
  Cooperative multi-agent reinforcement learning (MARL) faces significant scalability issues due to state and action spaces that are exponentially large in the number of agents. We propose a novel, value-based multi-agent algorithm called LOMAQ, which incorporates local rewards in the Training Decentralized Execution paradigm.
  arXiv  Detail & Related papers  (2021-09-22T10:08:15Z)
• Competing Adaptive Networks [56.56653763124104]
  We develop an algorithm for decentralized competition among teams of adaptive agents. We present an application in the decentralized training of generative adversarial neural networks.
  arXiv  Detail & Related papers  (2021-03-29T14:42:15Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.