Reinforcement learning (RL) and supervised learning are both important techniques in the field of machine learning, but they serve different purposes and operate in unique ways. Supervised learning focuses on learning a mapping from input data to output labels based on a labeled dataset. This process involves training a model on a known dataset where the correct answers are provided, allowing the model to predict outcomes for unseen data. In contrast, reinforcement learning is about training an agent to make decisions by interacting with an environment. Instead of having labeled outputs, the agent learns by receiving feedback as rewards or penalties based on its actions, guiding it toward optimal behavior.
The training process differs significantly between the two methods. In supervised learning, the model is trained on a fixed dataset in multiple rounds, adjusting its parameters to minimize prediction error. For example, if you were building a spam filter, you would use a labeled dataset of emails (spam or not spam) so the model learns which features indicate spam. Conversely, in reinforcement learning, the agent explores the environment, takes actions, and learns from the outcomes of those actions. For instance, in a game like chess, an RL agent plays many games, learning from victories and losses, ultimately developing strategies to improve its performance over time.
Another key difference is the nature of feedback received during training. In supervised learning, feedback is direct and immediate since the model is trained on explicit examples with known outputs. The model can clearly see how close its predictions are to the actual labels. On the other hand, in reinforcement learning, feedback is often delayed. An agent may perform actions that do not yield immediate rewards or penalties, making it necessary for the agent to learn from a longer sequence of actions. This could involve a complex series of decisions where the reward for one action might only be fully understood after several subsequent actions. Overall, while both methods aim at improving performance, they do so through fundamentally different approaches to learning and feedback.