Vous voulez être sûr que vos cadeaux seront sous le sapin de Noël à temps? Nos magasins vous accueillent à bras ouverts. La plupart de nos magasins sont ouverts également les dimanches, vous pouvez vérifier les heures d'ouvertures sur notre site.
- Retrait gratuit dans votre magasin Club
- 7.000.000 titres dans notre catalogue
- Payer en toute sécurité
- Toujours un magasin près de chez vous
Vous voulez être sûr que vos cadeaux seront sous le sapin de Noël à temps? Nos magasins vous accueillent à bras ouverts. La plupart de nos magasins sont ouverts également les dimanches, vous pouvez vérifier les heures d'ouvertures sur notre site.
- Retrait gratuit dans votre magasin Club
- 7.000.0000 titres dans notre catalogue
- Payer en toute sécurité
- Toujours un magasin près de chez vous
Hands-On Reinforcement Learning with Python
Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
Sudharsan Ravichandiran
Livre broché | Anglais
39,45 €
+ 78 points
Description
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 Description:
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.
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 for:
If 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.
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 318
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9781788836524
- Date de parution :
- 28-06-18
- Format:
- Livre broché
- Format numérique:
- Trade paperback (VS)
- Dimensions :
- 190 mm x 235 mm
- Poids :
- 548 g
