Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks
This addresses resource competition and communication constraints in edge computing systems, offering a practical solution for autonomous agents, though it is incremental as it builds on existing MARL methods.
The paper tackles the problem of task offloading in wireless edge networks by proposing a decentralized multi-agent reinforcement learning framework with implicit coordination through shared constraints, resulting in improved performance over centralized and independent baselines, especially in large-scale settings.
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.