Learning Through Experience

Unlike supervised learning, which relies on labeled examples, Reinforcement Learning (RL) is about trial and error in interactive environments. Like a child learning to ride a bike, RL agents improve by taking actions, observing outcomes, and adjusting based on rewards or penalties.

This approach has powered AI milestones—systems that beat humans at Chess, Go, and StarCraft, algorithms that optimize traffic and energy use, and even the training methods behind ChatGPT. RL bridges passive pattern recognition with active decision-making in dynamic settings.

The key idea is that agents learn directly from interaction, uncovering strategies not explicitly programmed. This makes RL vital for problems without clear solutions or in changing environments, enabling AI systems to act, adapt, and improve autonomously. Within the broader landscape, RL is a core branch of machine learning, often implemented with deep learning techniques.