Reinforcement-Learning-with-Python

Hands-On-Reinforcement-Learning-with-Python

This is the code repository for [Hands-On-Reinforcement-Learning-with-Python] Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow ##What is this book about? Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. This book covers the following exciting features:

• Understand the basics of reinforcement learning methods, algorithms, and elements
• Train an agent to walk using OpenAI Gym and Tensorflow
• Understand the Markov Decision Process, Bellman’s optimality, and TD learning
• Solve multi-armed-bandit problems using various algorithms
• Master deep learning algorithms, such as RNN, LSTM, and CNN with applications

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

policy_iteration():
Initialize random policy
for i in no_of_iterations:
Q_value = value_function(random_policy)
new_policy = Maximum state action pair from Q value

Following is what you need for this book: If you’re a machine learning developer or deep learning enthusiast interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Some knowledge of linear algebra, calculus, and the Python programming language will help you understand the concepts covered in this book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

Software and Hardware List

Chapter	Software required	OS required
1-12	anaconda	windows or mac
	chrome	windows or mac

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