Introduction to Applications Using Reinforcement Learning
( You can find the Korean version of the post at this link. )
Reinforcement learning technology has been one of the machine learning techniques that was relatively difficult to integrate into everyday life. However, recently, as reinforcement learning is being applied to various fields, machines are realizing the role of exploring paths that humans might take in many areas.
In this article, we will briefly review the basics of reinforcement learning and then look at case studies in each field.
Real-world Applications of Reinforcement Learning: From Theory to Reality
The advancement of artificial intelligence is changing every aspect of our daily lives. Among these, Reinforcement Learning (RL) is a particularly noteworthy field. RL is attracting great interest as it involves AI agents learning through trial and error, similar to the human learning process.
A Brief Review of Reinforcement Learning
The basic concept of RL is simple. An agent interacts with the environment and receives rewards or penalties each time it takes a specific action. By repeating this process, the agent learns strategies to maximize rewards.
The main components are as follows:
- Agent: The entity that learns and makes decisions
- Environment: The world with which the…
