Python Reinforcement Learning

Book Description

Apply modern reinforcement learning and deep reinforcement learning methods using and its powerful libraries

Key Features

  • Your entry point into the world of artificial using the power of Python
  • An example-rich guide to master various RL and DRL algorithms
  • Explore the power of modern Python libraries to gain confidence in building self-trained applications

Book Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and programming, including value and policy iteration. You'll also work on various datasets including image, text, and . This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.

By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.

This Learning Path includes content from the following Packt products:

  • Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran
  • Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani

What you will learn

  • Train an agent to walk using OpenAI Gym and TensorFlow
  • Solve multi-armed-bandit problems using various algorithms
  • Build intelligent agents using the DRQN algorithm to play the Doom game
  • Teach your agent to play Connect4 using AlphaGo Zero
  • Defeat Atari arcade using the value iteration method
  • Discover how to deal with discrete and continuous action spaces in various environments

Who this book is for

If you're an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.

Table of Contents

  1. Introduction to Reinforcement Learning
  2. Getting Started with OpenAI and TensorFlow
  3. The Markov Decision Process and Dynamic Programming
  4. Gaming with Monte Carlo Methods
  5. Temporal Difference Learning
  6. Multi-Armed Bandit Problem
  7. Playing Atari Games
  8. Atari Games with Deep Q Network
  9. Playing Doom with a Deep Recurrent Q Network
  10. The Asynchronous Advantage Actor Critic Network
  11. Policy Gradients and Optimization
  12. Balancing CartPole
  13. Simulating Control Tasks
  14. Building Virtual Worlds in Minecraft
  15. Learning to Play Go
  16. Creating a Chatbot
  17. Generating a Deep Learning Image Classifier
  18. Predicting Future Stock Prices
  19. Capstone - Car Racing Using DQN
  20. Looking Ahead

Book Details

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