Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
Abstract
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the complex system scheduling problem. Traditional scheduling methods often struggle to adapt to dynamic and intricate system environments, leading to suboptimal performance. This research proposes a novel framework leveraging MARL to address these challenges. The core idea involves deploying multiple agents, each responsible for scheduling a specific portion of the complex system. These agents operate independently, learning optimal scheduling policies through interaction with the environment and a carefully designed reward function. The system's overall efficiency and performance are enhanced through the coordinated learning and adaptation of these individual agents. The key contribution lies in the intelligent coordination of agents within a reinforcement learning framework, resulting in improved scheduling outcomes. This approach offers a scalable solution for managing complex systems with high degrees of variability and dynamism.
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