Items related to Reinforcement Learning Algorithms with Python: Learn,...
Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges - Softcover
Synopsis
Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and libraries
Key Features
- Learn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasks
- Understand and develop model-free and model-based algorithms for building self-learning agents
- Work with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategies
Book Description
Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents.
Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS.
By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.
What you will learn
- Develop an agent to play CartPole using the OpenAI Gym interface
- Discover the model-based reinforcement learning paradigm
- Solve the Frozen Lake problem with dynamic programming
- Explore Q-learning and SARSA with a view to playing a taxi game
- Apply Deep Q-Networks (DQNs) to Atari games using Gym
- Study policy gradient algorithms, including Actor-Critic and REINFORCE
- Understand and apply PPO and TRPO in continuous locomotion environments
- Get to grips with evolution strategies for solving the lunar lander problem
Who this book is for
If you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You'll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary.
Table of Contents
- The Landscape of Reinforcement Learning
- Implementing RL Cycle and OpenAI Gym
- Solving Problems with Dynamic Programming
- Q learning and SARSA Applications
- Deep Q-Network
- Learning Stochastic and DDPG optimization
- TRPO and PPO implementation
- DDPG and TD3 Applications
- Model-Based RL
- Imitation Learning with the DAgger Algorithm
- Understanding Black-Box Optimization Algorithms
- Developing the ESBAS Algorithm
- Practical Implementation for Resolving RL Challenges
"synopsis" may belong to another edition of this title.
About the Author
Andrea Lonza is a deep learning engineer with a great passion for artificial intelligence and a desire to create machines that act intelligently. He has acquired expert knowledge in reinforcement learning, natural language processing, and computer vision through academic and industrial machine learning projects. He has also participated in several Kaggle competitions, achieving high results. He is always looking for compelling challenges and loves to prove himself.
"About this title" may belong to another edition of this title.
Search results for Reinforcement Learning Algorithms with Python: Learn,...
Stock Image
Reinforcement Learning Algorithms with Python
Seller: Chiron Media, Wallingford, United Kingdom
Seller rating 5 out of 5 stars
PF. Condition: New. Seller Inventory # 6666-IUK-9781789131116
Quantity: 10 available
Stock Image
Reinforcement Learning Algorithms with Python
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9781789131116_new
Quantity: Over 20 available
Seller Image
Reinforcement Learning Algorithms with Python
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 38633942-n
Quantity: Over 20 available
Stock Image
Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges
Print on DemandSeller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9781789131116
Quantity: Over 20 available
Seller Image
Reinforcement Learning Algorithms with Python
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 38633942
Quantity: Over 20 available
Stock Image
Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges
Published by
Packt Publishing Limited, 2019
ISBN 10: 1789131111
ISBN 13: 9781789131116
New
Paperback / softback
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 785. Seller Inventory # C9781789131116
Quantity: Over 20 available
Stock Image
Reinforcement Learning Algorithms with Python
Print on DemandSeller: PBShop.store US, Wood Dale, IL, U.S.A.
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9781789131116
Quantity: Over 20 available
Seller Image
Reinforcement Learning Algorithms with Python
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition. Seller Inventory # 38633942
Quantity: Over 20 available
Seller Image
Reinforcement Learning Algorithms with Python
Published by
Packt Publishing Limited, GB, 2019
ISBN 10: 1789131111
ISBN 13: 9781789131116
New
Paperback
Seller: Rarewaves.com UK, London, United Kingdom
Seller rating 4 out of 5 stars
Paperback. Condition: New. Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and librariesKey FeaturesLearn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasksUnderstand and develop model-free and model-based algorithms for building self-learning agentsWork with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategiesBook DescriptionReinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements. This book will help you master RL algorithms and understand their implementation as you build self-learning agents.Starting with an introduction to the tools, libraries, and setup needed to work in the RL environment, this book covers the building blocks of RL and delves into value-based methods, such as the application of Q-learning and SARSA algorithms. You'll learn how to use a combination of Q-learning and neural networks to solve complex problems. Furthermore, you'll study the policy gradient methods, TRPO, and PPO, to improve performance and stability, before moving on to the DDPG and TD3 deterministic algorithms. This book also covers how imitation learning techniques work and how Dagger can teach an agent to drive. You'll discover evolutionary strategies and black-box optimization techniques, and see how they can improve RL algorithms. Finally, you'll get to grips with exploration approaches, such as UCB and UCB1, and develop a meta-algorithm called ESBAS.By the end of the book, you'll have worked with key RL algorithms to overcome challenges in real-world applications, and be part of the RL research community.What you will learnDevelop an agent to play CartPole using the OpenAI Gym interfaceDiscover the model-based reinforcement learning paradigmSolve the Frozen Lake problem with dynamic programmingExplore Q-learning and SARSA with a view to playing a taxi gameApply Deep Q-Networks (DQNs) to Atari games using GymStudy policy gradient algorithms, including Actor-Critic and REINFORCEUnderstand and apply PPO and TRPO in continuous locomotion environmentsGet to grips with evolution strategies for solving the lunar lander problemWho this book is forIf you are an AI researcher, deep learning user, or anyone who wants to learn reinforcement learning from scratch, this book is for you. You'll also find this reinforcement learning book useful if you want to learn about the advancements in the field. Working knowledge of Python is necessary. Seller Inventory # LU-9781789131116
Quantity: Over 20 available
Stock Image
Reinforcement Learning Algorithms with Python
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9781789131116
Quantity: Over 20 available