Editing How to teach AI to play Games: Deep Reinforcement Learning

== Reinforcement Learning: The Game-Changing AI ==

=== What's the Big Idea? ===
This article explores Reinforcement Learning (RL), a type of artificial intelligence that learns by doing. Imagine a computer program that learns to play chess by playing thousands of games and figuring out what moves work best. That's RL in action, and this article explains why it's a game-changer.

=== How Does RL Work? ===
In RL, a computer program, called an "agent," interacts with an environment to achieve a goal. The agent makes decisions, called "actions," and gets feedback, known as "rewards," based on how well it's doing. The agent learns to make better decisions by figuring out which actions lead to higher rewards. It's like teaching a dog new tricks by giving it treats when it does something right.

=== Why is RL Important? ===
RL is super versatile. It's used in video games, robotics, finance, healthcare, and more. For example, RL algorithms help robots learn to walk and pick up objects. They're also used in stock trading to predict market trends. The possibilities are endless, and that's what makes RL so exciting.

=== What Are the Challenges? ===
RL is powerful but not easy to use. It requires a lot of data and computing power. Also, if the rewards aren't set up correctly, the agent might learn the wrong things. For example, if a robot is rewarded for speed but not safety, it might learn to move fast but not safely.

=== In Short ===
Reinforcement Learning is a versatile and powerful form of AI that learns from experience. It has the potential to revolutionize many fields, but it also has its challenges, like the need for lots of data and the risk of learning the wrong things.

== Author Contributions ==
* '''Richard S. Sutton''' - Developed the core principles of RL.
* '''Andrew G. Barto''' - Worked on the algorithms that make RL possible.
* '''Satinder Singh''' - Focused on the applications of RL in real-world scenarios.

== Key References ==
For more in-depth information, refer to the original paper. Here are two key references:

1. ''Reinforcement Learning: An Introduction'' - This book by Sutton and Barto is considered the RL bible.
2. ''Playing Atari with Deep Reinforcement Learning'' - This paper showed how RL can be used to play video games at a superhuman level.

== External Links ==
[https://www.aaai.org/Papers/JAIR/VolIR-4-237.pdf Source URL]

[[Category:Machine Learning]]
[[Category:Computer Science]]