Reinforcement learning: In reinforcement learning, we study true learning agents. We need to define the agent, the environment, and a reward system. The agent must learn to explore the environment, while also taking actions to maximize its rewards. Its actions may also change the environment, which the agent should take into account

Reinforcement learning is aimed at acquiring the generalization ability in the same way as supervised learning, but the supervisor does not directly give answers to the student's questions. Instead, the supervisor evaluates the student's behavior and gives feedback about it. The objective of reinforcement learning is, based on the feedback from the supervisor, to let the student improve his/her behavior to maximize the supervisor's evaluation.

Reinforcement learning is an important model of the behavior of humans and robots, and it has been applied to various areas such as autonomous robot control, computer games, and marketing strategy optimization. Behind reinforcement learning, supervised and unsupervised learning methods such as regression, classification, and clustering are often utilized