AI RESEARCH
Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach
arXiv CS.AI
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ArXi:2603.24738v1 Announce Type: cross Efficient task scheduling in large-scale distributed systems presents significant challenges due to dynamic workloads, heterogeneous resources, and competing quality-of-service requirements. Traditional centralized approaches face scalability limitations and single points of failure, while classical heuristics lack adaptability to changing conditions. This paper proposes a decentralized multi-agent deep reinforcement learning (DRL-MADRL) framework for task scheduling in heterogeneous distributed systems.