transport gratuit dec21 desk

MAI SUNT 00:00:00:00

MAI SUNT

X

transport gratuit dec21 mob

MAI SUNT 00:00:00:00

MAI SUNT

X

Promotii popup img

TRANSPORT GRATUIT

la orice comanda, prin curier rapid,

oriunde în România!

Pregateste cadourile pentru cei dragi!
Close

Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web

De (autor): Maxim Lapan

Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web - Maxim Lapan

Deep Reinforcement Learning Hands-On - Second Edition: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web

De (autor): Maxim Lapan


New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features

  • Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms

Book Description

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

What you will learn

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft's TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik's Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques

Who this book is for

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

Citeste mai mult

transport gratuit

-15%

169.96Lei

199.95 Lei

Sau 16996 de puncte

!

Fiecare comanda noua reprezinta o investitie pentru viitoarele tale comenzi. Orice comanda plasata de pe un cont de utilizator primeste in schimb un numar de puncte de fidelitate, In conformitate cu regulile de conversiune stabilite. Punctele acumulate sunt incarcate automat in contul tau si pot fi folosite ulterior, pentru plata urmatoarelor comenzi.

Livrare in 3-5 saptamani

Descrierea produsului


New edition of the bestselling guide to deep reinforcement learning and how it's used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Key Features

  • Second edition of the bestselling introduction to deep reinforcement learning, expanded with six new chapters
  • Learn advanced exploration techniques including noisy networks, pseudo-count, and network distillation methods
  • Apply RL methods to cheap hardware robotics platforms

Book Description

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.

What you will learn

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft's TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik's Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques

Who this book is for

Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL

Citeste mai mult

Detaliile produsului

De pe acelasi raft

Parerea ta e inspiratie pentru comunitatea Libris!

Noi suntem despre carti, si la fel este si Newsletter-ul nostru.

Aboneaza-te si primesti un cupon de 10% pe care să-l folosesti la urmatoarea ta comanda! In plus, vei afla rapid care sunt promotiile zilei, noutatile si recomandarile noastre.

Ma abonez image one
Ma abonez image one