What is reinforcement learning?

Reinforcement learning is a type of machine learning where an agent learns to make decisions in an environment by receiving rewards or punishments for its actions. The agent interacts with the environment, learns over time through trial and error, and aims to maximize its cumulative rewards.

How reinforcement learning works

In reinforcement learning, the agent takes actions in the environment and receives feedback in the form of rewards or punishments. Based on this feedback, the agent adjusts its strategy to achieve long-term goals. Through the learning process, the agent refines its decision-making abilities to maximize rewards.

Applications of reinforcement learning

Reinforcement learning has various applications in AI technology, such as game playing, robotics, recommendation systems, self-driving cars, and more. It is particularly useful in scenarios where there is a need for sequential decision making and an element of uncertainty.

Differences from other types of machine learning

Reinforcement learning differs from other types of machine learning, such as supervised learning and unsupervised learning, in that it involves an agent interacting with an environment to learn through trial and error. Supervised learning, for example, relies on labeled data for training, while reinforcement learning learns from rewards and punishments.