PyTorch 1.0 Reinforcement Learning Cookbook

Yuxi (Hayden) Liu 저자(글)

Packt Publishing · 2020년 02월 01일

책 소개

Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use.

With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game.

By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems.

작가정보

저자(글) Yuxi (Hayden) Liu

구글의 머신러닝 소프트웨어 엔지니어. 구글 이전에는 여러 데이터 기반 도메인에서 머신러닝 과학자로 일하며 디지털 광고, 마케팅, 사이버 보안에 관한 전문 지식을 발휘했다. 현재 세계에서 가장 큰 검색 엔진에서 광고 최적화를 위한 머신러닝 모델과 시스템을 개발하고 개선하는 일을 한다. 교육에 대한 열정이 커 여러 머신러닝 도서를 집필했다. 첫 책인 《Python Machine Learning By Example》의 초판은 2017년과 2018년에 아마존에서 베스트셀러 1위에 올랐으며 다양한 언어로 번역되었다. 집필한 책으로는 《Machine Learning with PyTorch and Scikit-Learn》(2022), 《Deep Learning with R for Beginners》(2019), 《Hands-On Deep Learning Architectures with Python》(2019), 《R Deep Learning Projects》(이상 Packt, 2018)가 있다.

목차

• Getting started with reinforcement learning and PyTorch
  Markov Decision Process and Dynamic Programming
  Monte Carlo Methods for making numerical estimations
  Temporal Difference and Q-Learning
  Solving Multi Armed Bandit problems
  Scaling up Learning with Function Approximation
  Deep Q-Networks in Action
  Implementing Policy Gradients and Policy Optimization
  Capstone Project: Playing Flappy Bird with DQN

기본정보

ISBN	9781838551964 ( 1838551964 )
발행(출시)일자	2020년 02월 01일
쪽수	327쪽
크기	190 * 236 * 19 mm / 630 g
총권수	1권
언어	영어