Reinforcement learning is an advanced subfield of machine learning, often classified under unsupervised approaches. It involves models that learn through trial and error to reach an objective by constantly incorporating feedback and adjusting their actions based on the outcomes of previous runs. The model is taught to optimize its performance over time by self-correcting based on assessments of its past actions. An example implementation is a robotics system like Boston Dynamics, where a robot must learn the exact configurations needed to complete a mechanical task, such as standing up after a fall.