Understanding Reinforcement Learning with Neural Networks Part 4: Positive and Negative Rewards

This is a tutorial series entry covering how positive and negative reward signals work in a neural-network-based RL setup. It sits firmly in the educational content bucket — the audience is someone learning RL from scratch, not a team evaluating a production tool. The writing is approachable and the series format means prior context is assumed from earlier installments, so jumping in at part four cold will leave some gaps. There is no new research here and no novel framing that distinguishes it from the dozens of RL explainer series available elsewhere. The reason to surface it at all is that teams who hire junior engineers or onboard non-ML colleagues into AI projects sometimes need a plain-language reading list, and this fits that slot. Do not expect a shortcut to production RL knowledge — it is genuinely introductory. -> Best for: ML researcher or AI engineer looking for onboarding material to share with a non-specialist teammate