The Reward Mechanism: How Neural Networks Learn from Success and Failure

This article explains how reinforcement learning leverages positive and negative rewards to guide neural network training by multiplying derivatives with reward values. This mechanism enables networks to reinforce successful decisions while learning to avoid poor choices—a fundamental principle that powers intelligent AI systems. Understanding this concept is essential for developing AI that effectively learns from real-world feedback and outcomes.