[国外教程] 游戏AI人工智能深度学习训练视频教程



Unity视频教程是由AE资源素材社区整理出品的游戏AI人工智能深度学习训练视频教程,时长:12小时30分,大小:5 GB,MP4高清视频格式,教程使用软件:Unity,作者:Jan Warchocki,共118个章节,语言:英语。

Unity是一款跨平台的游戏开发工具,从一开始就被设计成易于使用的产品。作为一个完全集成的专业级应用,Unity还包含了价值数百万美元的功能强大的游戏引擎。Unity 4作为一个游戏开发工具,它的设计主旨就是让你能够创建令人惊讶的游戏。如果你之前尝试过Unity,请查看Unity 3都做了哪些更新。如果你是第一次尝试,那就好好看看吧,看Unity都能为你带来什么。Unity作为一种开发环境,可让你脱离传统的游戏开发方式,以一种更简单的方式专注于你的游戏开发。开发网络游戏、移动游戏、单机游戏,Unity都能完全胜任。

Learn how to use powerful Deep Reinforcement Learning and Artificial Intelligence tools on examples of AI simple games!
What you'll learn
SOLVE THE TRAVELLING SALESMAN PROBLEM
Understand and implement Genetic Algorithms
Get the general AI framework
Understand how to use this tool to your own projects
SOLVE A COMPLEX MAZE
Understand and implement Q-Learning
Get the right Q-Learning intuition
Understand how to use this tool to your own projects
SOLVE MOUNTAIN CAR FROM OPENAI GYM
Understand and implement Deep Q-Learning
Build Artificial Neural Networks with Keras
Use the environments provided in OpenAI Gym
Understand how to use this tool to your own projects
SOLVE SNAKE
Understand and implement Deep Convolutional Q-Learning
Build Convolutional Neural Networks with Keras
Understand how to use this tool to your own projects
Requirements
High school maths
Basic knowledge of programming, such as "if" conditions, "for" and "while" loops, etc.
Description
Ever wish you could harness the power of Deep Learning and Machine Learning to craft intelligent bots built for gaming?
If you’re looking for a creative way to dive into Artificial Intelligence, then ‘Artificial Intelligence for Simple Games’ is your key to building lasting knowledge.
Learn and test your AI knowledge of fundamental DL and ML algorithms using the fun and flexible environment of simple games such as Snake, the Travelling Salesman problem, mazes and more.
1. Whether you’re an absolute beginner or seasoned Machine Learning expert, this course provides a solid foundation of the basic and advanced concepts you need to build AI within a gaming environment and beyond.
2. Key algorithms and concepts covered in this course include: Genetic Algorithms, Q-Learning, Deep Q-Learning with both Artificial Neural Networks and Convolutional Neural Networks.
3. Dive into SuperDataScience’s much-loved, interactive learning environment designed to build knowledge and intuition gradually with practical, yet challenging case studies.
4. Code flexibility means that students will be able to experiment with different game scenarios and easily apply their learning to business problems outside of the gaming industry.
‘AI for Simple Games’ Curriculum
Section #1 — Dive into Genetic Algorithms by applying the famous Travelling Salesman Problem to an intergalactic game. The challenge will be to build a spaceship that travels across all planets in the shortest time possible!
Section #2 — Learn the foundations of the model-free reinforcement learning algorithm, Q-Learning. Develop intuition and visualization skills, and try your hand at building a custom maze and design an AI able to find its way out.
Section #3 — Go deep with Deep Q-Learning. Explore the fantastic world of Neural Networks using the OpenAI Gym development environment and learn how to build AIs for many other simple games!
Section #4 — Finish off the course by building your very own version of the classic game, Snake! Here you’ll utilize Convolutional Neural Networks by building an AI that mimics the same behavior we see when playing Snake.
Who this course is for:
Anyone interested in beginning their AI journey
Anyone interested in creating an AI for games
Anyone looking for flexible tools to solve many kinds of Artificial Intelligence problems
A data science enthusiast looking to expand their knowledge of AI



游戏AI人工智能深度学习训练视频教程  AE资源素材社区 www.aeziyuan.com

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