The Download: For Adaptive Decision Making
The Download: For Adaptive Decision Making
Leveraging reinforcement learning to optimize processes and reduce defects
- Publication:
- Quality Progress
- Date:
- March 2026
- Issue:
- Volume 59 Issue 3
- Pages:
- pp. 46-49
- Author(s):
- Mateos, Matthew C.
Abstract
Reinforcement learning (RL) is best suited for scenarios in which an agent must learn optimal actions through trial and error by interacting with a dynamic environment to maximize cumulative rewards, rather than relying on labeled or unlabeled static data. This approach enhances Six Sigma and quality management by enabling adaptive, real-time decision making that supports continuous improvement. By leveraging RL, organizations can optimize processes, reduce defects and continuously enhance performance based on feedback from the environment.
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