Deep Reinforcement Learning: Write Bots in Python that can Play Games
Deep Reinforcement Learning: Write Bots in Python that can Play Games
About the Book
Reinforcement Learning (RL) is an exciting new field of machine learning, in which bots learn by playing games.
It is different from traditional machine learning (supervised or unsupervised) in that there are no training samples with expected outputs.
In this book, we'll learn RL by programming bots that can play games. We will go over basic Q learning techniques, as well as Deep Reinforcement Learning, that uses Neural Networks.
My book Build Your Own Neural Network is a per-requisite.
About the Author
Table of Contents
-
Introduction to Reinforcement Learning
- Deep Reinforcement Learning(DRL) Today
- Pre-requisites
- A Virtual Machine to run the code
- If you get stuck….
-
Q Learning by Hand
- Q Learning
- Q Learning Matrixes
- The Q Learning Formula
- Hand Learning Q Learning
- Q Learning: The Code
- A Quick Intro to OpenAI
-
Simpler Methods of RL
- Random Search
- Hill climbing
-
Q Learning Example 2: Frozen Lake
- Winning the game
- A Quick Intro to Deep Reinforcement Learning
-
Build our own Deep Network
- Our Play and Train Function
- The Keras RL Library
-
More Learning Tools
- Introduction to VizDoom
- And so we come to an End
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