Multi‐Agent Reinforcement Learning for Concurrent Task‐Chain Scheduling in Heterogeneous Edge–Cloud Environments

Abstract

In edge–cloud collaborative computing, efficient scheduling of concurrent task chains is essential for reducing end‐to‐end latency. However, heterogeneous resources, dynamic link contention, and coupled routing‐computation decisions make it difficult to improve system efficiency while reducing communication conflicts. To address these challenges, we formulate concurrent heterogeneous task‐chain scheduling under partial observability as a multi‐agent partially observable Markov game and propose a MARL‐based scheduling method. Each task chain is represented by a mobile agent that makes routing, computation, data‐access, and waiting decisions based on local observations. Building upon a multi‐agent proximal policy optimization (MAPPO) architecture, we develop DC‐MAPPO, which introduces a Directional Clamp mechanism to improve policy update stability under high‐concurrency conditions. In addition, a dynamic action masking strategy is designed to ensure decision feasibility and reduce invalid exploration. Experimental results across multiple network topologies show that the proposed method outperforms the compared baselines in most tested settings. Under high‐load conditions, DC‐MAPPO reduces system makespan by 10.7%–17.1% and reduces the link reservation failure rate by 8.4–13.2 percentage points compared with MAPPO.