What is reinforcement learning?

Supervised learning relies on labeled data to make predictions, while reinforcement learning learns through interaction with an environment using rewards and penalties. Reinforcement learning focuses on decision making over time rather than predicting a correct output.

A reinforcement learning agent is the decision-making entity that observes the environment, takes actions, and learns from feedback. Its goal is to learn an optimal policy that maximizes cumulative rewards.

Rewards provide feedback that tells the agent how good or bad an action was in a given state. Over time, the reward signal guides the agent toward behaviors that lead to better long-term outcomes.

Deep reinforcement learning combines reinforcement learning with deep neural networks to process complex, high-dimensional input data like images or sensor signals. This allows agents to learn directly from raw data while optimizing decisions based on future rewards.

Yes, reinforcement learning can complement generative AI by optimizing decision-making processes, such as selecting actions or strategies, while generative models focus on producing content. Together, they are often used in systems that require both perception and adaptive control.