Deep Reinforcement Learning - Hands-On (with Python). Book Review

Deep Reinforcement Learning - Hands-On (with Python). Book Review

Deep Reinforcement Learning - Hands-On (with Python). Book Review - Apply modern #RL methods to practical problems of #chatbots, #robotics, discrete optimization, we automation, and more. The second edition of this book includes multi-agent methods and advanced exploration techniques.

Apply modern #RL methods to practical problems of #chatbots, #robotics, discrete optimization, we automation, and more. The second edition of this book includes multi-agent methods and advanced exploration techniques.

The chapters of the book are: #1 - What is Reinforcement Learning? (Contains an introduction to RL ideas and the main formal models). #2. OpenAI Gym (Introduces the practical aspects of RL, using the open source library Gym) #3. Deep Learning with PyTorch (Gives a quick overview of the PyTorch library) #4. The Cross-Entropy Method (Introduces one of the simplest methods in RL to give you an impression of RL methods and problems) #5. Tabular Learning and the Bellman Equation (Introduces the value-based family of RL methods) #6. Deep Q-Networks (Describes deep Q-networks (DQNs), an extension of the value-based methods, allowing to solve a complicated environment. #7. Higher-Level RL Libraries (Describes the library PTAN, which you will use to simplify the implementations of RL methods) #8. DQN Extensions (Gives a detailed overview of a modern extension to the DQN method, to improve its stability and convergence in complex environment) #9. Ways to Speed up RL Methods (Provides an overview of ways to make the execution of RL code faster) #10. Stocks Trading Using RL (The first practical project and focuses on applying the DQN method to stock trading) #11. Policy Gradients - an Alternative (Introduces another family of RL methods that is based on policy learning) #12. The Actor-Critic Method (Describes one of the most widely used methods in RL) #13. Asynchronous Advantage Actor-Critic (Extends the actor-critic method with parallel environment communication, which improves stability and convergence) #14. Training Chatbots with RL (The second project and shows how to apply RL methods to natural language processing NLP problems) #15. The TextWorld Environment (Covers the application of RL methods to interactive fiction games) #16. Web Navigation (Another long project that applies RL to web page navigation using the MiniWoB set of tasks) #17. Continuous Action Space (Describes the specifics of environments using continuous action spaces and various methods) #18. RL in Robotics (Covers the application of RL methods to robotics problems, including small hardware robot) #19. Trust Regions - PPO, TRPO, ACKTR, and SAC (Continuous action spaces describing the trust region set of methods) #20.Black-Box Optimization of RL (Shows another set of methods that do not use gradients in their explicit form) #21. Advances Exploration (Covers different approaches that can be used for better exploration of the environment) #22. Beyond Model-Free - Imagination (Introduces the model-based approach to RL and uses recent research results about imagination in RL) #23. AlphaGo Zero (Describes the AlphaGo Zero method and applies it to the game Connect 4) #24. RL in Discrete Optimization (Describes the application of RL methods to the domain of discrete optimization, using the Rubik’s Cube as an environment) #25. Multi-agent RL (Introduces a relatively new direction of RL methods for situations with multiple agents)

The book includes 3 practical Reinforcement Learning project that you can build from scratch on your own with Python.

Example code files available on Github: https://github.com/PacktPublishing/Deep-Reinforcement-Learning-Hands-On-Second-Edition Color Images of the book: https://static.packt-cdn.com/downloads/9781838826994_ColorImages.pdf

The content of this book review: 0:00 - Introduction 1:27 - Chapters 10:36 - How to use this book? 11:10 - Additional information (book price, etc.) 11:35 - Basic review and Final word

Other data science, machine learning and deep learning book reviews on my channel:

  1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 2nd Edition, by by Aurélien Géron (https://youtu.be/nTadOuomhck)
  2. Practical Deep Learning for Cloud, Mobile, and Edge. Real World AI & Computer Vision Projects Using Python, Keras and Tensorflow. (https://youtu.be/IJXrC3T1Wbc)

python

What is Geek Coin

What is GeekCash, Geek Token

Best Visual Studio Code Themes of 2021

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

top 30 Python Tips and Tricks for Beginners

In this post, we'll learn top 30 Python Tips and Tricks for Beginners

Lambda, Map, Filter functions in python

You can learn how to use Lambda,Map,Filter function in python with Advance code examples. Please read this article

Python Tricks Every Developer Should Know

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

How to Remove all Duplicate Files on your Drive via Python

Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.

Basic Data Types in Python | Python Web Development For Beginners

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.