Hands-On Reinforcement Learning with Python (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
318
Utgivningsdatum
2018-06-28
Förlag
Packt Publishing Limited
Illustrationer
Black & white illustrations
Dimensioner
240 x 192 x 20 mm
Vikt
625 g
Antal komponenter
1
Komponenter
403:B&W 7.5 x 9.25 in or 235 x 191 mm Perfect Bound on White w/Matte Lam
ISBN
9781788836524
Hands-On Reinforcement Learning with Python (häftad)

Hands-On Reinforcement Learning with Python

Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow

Häftad Engelska, 2018-06-28
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A hands-on guide enriched with examples to master deep reinforcement learning algorithms with Python Key Features 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 various state-of-the-art architectures along with math Book DescriptionReinforcement 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. 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 Who this book is forIf 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.
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De som köpt den här boken har ofta också köpt Python Reinforcement Learning av Sudharsan Ravichandiran, Sean Saito, Rajalingappaa Shanmugamani, Yang Wenzhuo (häftad).

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Övrig information

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.

Innehållsförteckning

Table of Contents Introduction to Reinforcement Learning Getting started with OpenAI and Tensorflow Markov Decision process and Dynamic Programming Gaming with Monte Carlo Tree Search Temporal Difference Learning Multi-Armed Bandit Problem Deep Learning Fundamentals Deep Learning and Reinforcement Playing Doom With Deep Recurrent Q Network Asynchronous Advantage Actor Critic Network Policy Gradients and Optimization Capstone Project - Car Racing using DQN Current Research and Next Steps