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Machine Learning in Game Design
Rating: 2.0 out of 5(1 rating)
44 students

Machine Learning in Game Design

Learn AI And reinforcement learning through the lens of game development in Unity.
Created byRobotics school Robotikos mokykla
Last updated 4/2023
English

What you'll learn

  • Students will learn to code with Unity at an intermediate level: they will learn basic programming concepts and how to create computer games.
  • Students will learn to train a simple Reinforcement Learning algorithm do a wanted task.
  • Student will be able to train combination of policy optimization algorithms to train an agent to walk and reach the objective
  • You will be able to create your own game environment and agent.
  • You will be able to train evolutionary algorithm to balance the ball on characters head.
  • Introduction to machine learning with python
  • Students will be able to train a combination of CNN and PPO algorithms.

Course content

4 sections • 22 lectures • 5h 31m total length

  • Introduction22:02

    Explore how machine learning enhances game design, with a focus on reinforcement learning, and learn to set up Python, PyTorch, Unity, and ML-Agents for hands-on development.

  • A welcome word1:17

    Discover how machine learning enhances game design by letting computers learn from experience, improving NPC behavior, and leveraging AI, computer vision, classification, and reinforcement learning in games.

  • Introduction to the course6:27
  • Installation & Software setup14:12

Requirements

  • Basic knowledge of programming
  • You will need a Desktop or Laptop computer.
  • You must be willing and eager to learn.
  • Beginner level of python.

Description

In this course you will be introduced to the basics of Machine Learning through game design in Unity and Python. First you will be introduced to the very basics of ML and python, you will learn such simple ML concepts such as supervised learning, regression and gradient descent. After that you will get acquainted with reinforcement learning and will try to understand and apply a combination of Reinforcement Learning, Deep Neural Networks and other algorithms in the Unity environment to help your agent accomplish complex and dynamic tasks. Lastly you will learn how to build dynamic full featured RL environments and agents for you to train later in your games. You will not only be able to create your own RL algorithms from scratch (such as Q-learning, SARSA and PPO), but also customize them to fit the needs of your environment and train your agents in Unity. You will gain experience with widely used tools and libraries in the industry of ML, such as: Unity3D, Pytorch, mlagents-learn, scikit-learn and more. We hope that this course will help you to better understand and prepare for your journey of developing truly intelligent agents and characters in your own games. Let's get started!

Who this course is for:

  • Beginner programmers who want to delve into AI.
  • For everyone who want to create their own reinforcement learning algorythms in Unity by using python.
  • For people who want to delve into game development and AI

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