Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach

Decentralized Task Scheduling in Distributed Systems: A Deep Reinforcement Learning Approach explores A decentralized multi-agent deep reinforcement learning framework for efficient task scheduling in distributed systems, offering significant improvements in completion time, energy efficiency, and SLA satisfaction.. Commercial viability score: 7/10 in Distributed Systems Scheduling.