What is reinforcement learning?

Reinforcement learning is a type of machine learning in which a system learns to make optimal decisions by interacting with its environment. At its core is the idea of reward, whereby the system is rewarded or punished based on the actions it performs in a given situation.

Procedure:

1. Exploration: The agent explores the environment to learn more about the effects of its actions.

2. Exploitation: The agent uses previous knowledge to select actions that are likely to lead to positive rewards.

3. Reward collection: The agent collects rewards based on its actions and adjusts its policy to maximize the total reward.

4. Learning process: Through repeated interaction with the environment, the agent improves its policy and becomes able to make optimal decisions.

Reinforcement learning has proven to be an effective method in complex and dynamic environments and is used in various fields for the development of intelligent, self-learning systems.