Understanding Reinforcement Learning with Neural Networks Part 6: Completing the Reinforcement Learning Process

A neural network was trained with reinforcement learning across inputs from 0 to 1 until its bias stabilized around -10.

After training, the model chose Place A when hunger was low and Place B when hunger was high.

The article also outlines the reinforcement learning update loop, showing how rewards guide gradient-based learning without fixed target outputs.

It closes by previewing RLHF as the next step in aligning model behavior.