How does reinforcement learning differ from supervised learning?

Reinforcement learning and supervised learning are both branches of machine learning, but they differ fundamentally in how they receive feedback, what objective they optimize, and how they interact with the environment. Supervised learning maps inputs to known correct outputs using labeled examples, while reinforcement learning seeks policies that maximize cumulative reward through trial and error.<br><br>Learning signals and feedback<br>Tom M. Mitchell at Carnegie Mellon University characterizes supervised learning as receiving explicit corrective answers for each example: the algorithm is shown input-output pairs and trains a function to predict the output from the input. In contrast, Richard S. Sutton at University of Alberta and Andrew G. Barto at University of Massachusetts Amherst characterize reinforcement learning as learning from scalar reward signals obtained through interaction. The feedback in supervised learning is immediate and local to each sample; the feedback in reinforcement learning is often delayed and global, requiring the algorithm to assign credit across sequences of actions. This difference creates distinct technical challenges. Supervised learning focuses on minimizing prediction error on labeled datasets and benefits from well-understood empirical risk minimization theory. Reinforcement learning must balance exploration and exploitation, manage nonstationary data distributions that arise when the learner’s actions change subsequent inputs, and solve credit assignment across time.<br><br>Causes and mechanisms of difference<br>The origin of the divergence lies in the problem formulation. Supervised learning assumes access to ground-truth labels and typically treats data as independent and identically distributed. Supervision can be supplied by human annotation, sensors, or legacy systems. Reinforcement learning assumes an agent that takes actions in an environment, receives observations and scalar rewards, and aims to maximize long-term return; this setup mirrors decision-making tasks where explicit labels for optimal actions are unavailable or infeasible to produce. As Sutton and Barto explain, algorithms for reinforcement learning therefore incorporate mechanisms for estimating value functions, learning policies, and planning when models of the environment are available.<br><br>Consequences in application and society<br>The practical consequences are significant. Supervised methods excel in classification and regression tasks like image recognition or medical diagnosis when labeled datasets exist. Reinforcement learning is suited to sequential decision problems such as robotics, game playing, and resource allocation where interaction and delayed outcomes matter. The different data requirements affect deployment: supervised models require curated labels, which can be costly and culturally sensitive when human judgments are involved; reinforcement learning requires safe exploration strategies, especially in real-world settings such as autonomous vehicles or healthcare, to avoid harmful actions during learning.<br><br>Human, cultural, and environmental nuances arise in both regimes. Reliance on labeled data can embed annotator biases into supervised systems; researchers and practitioners must consider who provides labels and under what norms. Reinforcement learning systems that learn from interaction can amplify unintended behaviors if reward functions poorly reflect societal values. Computational costs for large-scale reinforcement learning, reported in the broader machine learning literature, raise environmental concerns about energy consumption and access inequality, disproportionately affecting institutions and communities with fewer computational resources. Responsible development therefore combines technical safeguards, domain expertise, and stakeholder engagement to align learning objectives with human and cultural priorities.