Reinforcement learning (RL) differs from other machine learning paradigms primarily in how it learns from interactions rather than static datasets. In supervised learning, models are trained on labeled examples where each input has a corresponding correct output, such as classifying images or predicting house prices. Unsupervised learning identifies patterns in unlabeled data, like clustering customer segments or reducing data dimensions. RL, however, involves an agent learning to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, aiming to maximize cumulative rewards over time. For example, a game-playing AI like AlphaGo learns by playing millions of games, adjusting its strategy based on wins and losses, unlike a supervised model that would require a pre-labeled dataset of optimal moves.
A key distinction lies in the feedback mechanism. Supervised learning relies on immediate, explicit labels (e.g., “this image is a cat”), while RL deals with delayed, often sparse rewards. The agent might not know whether an action was good until many steps later, creating a credit assignment problem. Additionally, RL requires balancing exploration (trying new actions to discover rewards) and exploitation (using known effective actions). For instance, a robot learning to walk might experiment with different leg movements (exploration) but must eventually prioritize actions that keep it upright (exploitation). This contrasts with supervised learning, where the model follows a fixed dataset without needing to explore, or unsupervised learning, which lacks explicit feedback altogether.
RL’s applications and challenges also set it apart. It excels in dynamic environments where predefined rules are impractical, such as training autonomous vehicles or optimizing resource allocation in real-time systems. However, RL often requires significant computational resources and careful design of reward functions. A poorly designed reward can lead to unintended behaviors—for example, a recommendation system maximizing user clicks might inadvertently promote sensationalist content. Unlike supervised learning, where performance is measured against a validation set, RL success depends on the agent’s ability to adapt to unseen scenarios. These factors make RL powerful for sequential decision-making tasks but more complex to implement compared to other paradigms.