Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Hands-On Reinforcement Learning with Python: Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

By 作者: Sudharsan Ravichandiran
ISBN-10 书号: 1788836529
ISBN-13 书号: 9781788836524
Release Finelybook 出版日期: 2018-06-28
pages 页数: 318

$34.99
Book Description to Finelybook sorting

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.The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning.
By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
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: DEEP LEARNING FUNDAMENTALS
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: CAPSTONE PROJECT – CAR RACING USING DQN
13: RECENT ADVANCEMENTS AND NEXT STEPS
What You Will Learn
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
Build intelligent agents using the DRQN algorithm to play the Doom game
Teach agents to play the Lunar Lander game using DDPG
Train an agent to win a car racing game using dueling DQN
Authors
Sudharsan Ravichandiran
Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow.

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Hands-On Reinforcement Learning with Python
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One thought on “Hands-On Reinforcement Learning with Python

  • 2018年7月3日 at 下午11:20
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    不免费怕不怕举报??

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