Reinforcement learning – learning through rewards

Reinforcement learning is a machine learning approach in which a model, called an agent, learns optimal behavior through trial and error based on rewards and penalties for individual decisions in a given environment. It is used, for example, in gaming, robot control, and also in fine-tuning language models based on human feedback (RLHF), which helps models better align with users’ actual preferences.