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Learn to Play Atari

~ 1 hour
New

This project will help you understand the basics of reinforcement learning and how intelligent agents learn through trial and error. You'll apply these concepts in code, gaining practical experience by creating and training a Deep Q-Network (DQN) agent from scratch using Python and the Gymnasium library

Provided by

JetBrains Academy JetBrains Academy

About

Google's DeepMind created AlphaGo, an AI that used reinforcement learning to master the game of Go and defeat a world champion. Now, you can use similar techniques to teach computers to play classic Atari games. In this project, you will create a Deep Q-Network (DQN) agent to learn and play Breakout. You'll see how AI can learn to navigate and succeed in interactive environments, and you'll learn how these methods can be applied to other games and simulations.

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Training project

This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.

What you'll learn

Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
This stage involves setting up the Gymnasium environment get familiar with the game of Breakout, and learn the basics of interacting with it using code.
In this stage you'll build the brain of our AI agent – a Deep Q-Network – which will learn to make decisions in the game. To achieve this we will use the stable_baselines3 library.
In this stage, you'll train the agent using Stable Baselines3. The agent will learn from its successes and failures in Breakout by running through multiple episodes and updating its strategy based on the rewards it receives.
Time to see our AI in action! We'll analyze its Breakout skills by observing its gameplay and evaluating its performance based on how well it scores.