Reinforcement learning (RL) differs from other machine learning paradigms, such as supervised and unsupervised learning, in the way learning occurs. In supervised learning, models learn from labeled datasets, where the input-output pairs are predefined, and the model’s goal is to map inputs to correct outputs. In contrast, RL involves an agent that interacts with an environment, where correct outputs (rewards) are not immediately provided but are instead learned through exploration and feedback.
Unsupervised learning, on the other hand, focuses on finding hidden patterns or structures in data without explicit labels. Unlike RL, it doesn’t involve sequential decision-making. RL also differs in its focus on long-term decision-making, where the agent learns strategies to maximize cumulative rewards over time, while supervised learning typically aims for immediate accuracy in predictions.
Another key difference is that RL involves the concept of delayed feedback. The agent might not know the outcome of its actions immediately but must rely on a reward signal that helps it understand how well it performed in the long run.