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Python Reinforcement Learning
Python Reinforcement Learning

Python Reinforcement Learning: Solve complex real-world problems by mastering reinforcement learning algorithms using OpenAI Gym and TensorFlow

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Profile Icon Sudharsan Ravichandiran Profile Icon Yang Wenzhuo Profile Icon Rajalingappaa Shanmugamani Profile Icon Sean Saito
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Course Apr 2019 496 pages 1st Edition
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Profile Icon Sudharsan Ravichandiran Profile Icon Yang Wenzhuo Profile Icon Rajalingappaa Shanmugamani Profile Icon Sean Saito
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Course Apr 2019 496 pages 1st Edition

Key benefits

  • Your entry point into the world of artificial intelligence 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

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 dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. 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

Who is this book 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.

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 games using the value iteration method
  • Discover how to deal with discrete and continuous action spaces in various environments

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Apr 18, 2019
Length: 496 pages
Edition : 1st
Language : English
ISBN-13 : 9781838649777
Vendor :
Google
Category :
Languages :
Tools :

Product Details

Publication date : Apr 18, 2019
Length: 496 pages
Edition : 1st
Language : English
ISBN-13 : 9781838649777
Vendor :
Google
Category :
Languages :
Tools :

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Table of Contents

20 Chapters
Introduction to Reinforcement Learning Chevron down icon Chevron up icon
Getting Started with OpenAI and TensorFlow Chevron down icon Chevron up icon
The Markov Decision Process and Dynamic Programming Chevron down icon Chevron up icon
Gaming with Monte Carlo Methods Chevron down icon Chevron up icon
Temporal Difference Learning Chevron down icon Chevron up icon
Multi-Armed Bandit Problem Chevron down icon Chevron up icon
Playing Atari Games Chevron down icon Chevron up icon
Atari Games with Deep Q Network Chevron down icon Chevron up icon
Playing Doom with a Deep Recurrent Q Network Chevron down icon Chevron up icon
The Asynchronous Advantage Actor Critic Network Chevron down icon Chevron up icon
Policy Gradients and Optimization Chevron down icon Chevron up icon
Balancing CartPole Chevron down icon Chevron up icon
Simulating Control Tasks Chevron down icon Chevron up icon
Building Virtual Worlds in Minecraft Chevron down icon Chevron up icon
Learning to Play Go Chevron down icon Chevron up icon
Creating a Chatbot Chevron down icon Chevron up icon
Generating a Deep Learning Image Classifier Chevron down icon Chevron up icon
Predicting Future Stock Prices Chevron down icon Chevron up icon
Capstone Project - Car Racing Using DQN Chevron down icon Chevron up icon
Looking Ahead Chevron down icon Chevron up icon
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