Escucha y lee

Entra en un mundo infinito de historias

  • Vive la experiencia de leer y escuchar todo lo que quieras
  • Más de 650.000 títulos
  • Títulos en exclusiva y Storytel Originals
  • Primeros 14 días gratis, luego 8,99 €/mes
  • Cancela cuando quieras
Suscríbete ahora
Details page - Device banner - 894x1036

Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges

Idioma
Inglés
Format
Categoría

No ficción

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.

© 2019 Packt Publishing (ebook ): 9781789139709

Fecha de lanzamiento

ebook : 18 de octubre de 2019

Otros también disfrutaron ...

  1. Deep Learning John D. Kelleher
  2. Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World Karim R. Lakhani
  3. Machine Learning: Deep Learning, Text Analytics, and Reinforcement Learning with Big Data David Feldspar
  4. The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity Byron Reese
  5. Artificial Intelligence For Dummies Luca Massaron
  6. Deep Learning: Guide to Machine Learning and Artificial Intelligence David Feldspar
  7. Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are Seth Stephens-Davidowitz
  8. The Formula: How Algorithms Solve all our Problems... and Create More Luke Dormehl
  9. Everyday Calculus: Discovering the Hidden Math All around Us Oscar E. Fernandez
  10. Reinforcement Learning: Machine Learning, Gamma, and Inventory Management David Feldspar
  11. The Deep Learning Revolution Terrence J. Sejnowski
  12. Uberland: How Algorithms Are Rewriting the Rules of Work Alex Rosenblat
  13. AI and Machine Learning for On-Device Development: A Programmer's Guide, 1st Edition Laurence Moroney
  14. Python: - The Bible- 3 Manuscripts in 1 book: Python Programming for Beginners - Python Programming for Intermediates - Python Programming for Advanced Maurice J. Thompson
  15. Data Science John D. Kelleher
  16. Human Universe Professor Brian Cox
  17. Python Machine Learning for Beginners: Perfect guide on How to Become a Successful Data Scientist Alex Campbell
  18. The Princeton Companion to Mathematics Timothy Gowers
  19. Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling William J. Stewart
  20. How Smart Machines Think Sean Gerrish
  21. Artificial Intelligence: The Insights You Need from Harvard Business Review Andrew McAfee
  22. On the Future: Prospects for Humanity Martin Rees
  23. Standard Deviations: Flawed Assumptions, Tortured Data, and Other Ways to Lie with Statistics Gary Smith
  24. AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence Laurence Moroney
  25. Automate This: How Algorithms Came to Rule Our World Christopher Steiner
  26. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence Kate Crawford
  27. Homo Deus: A Brief History of Tomorrow Yuval Noah Harari
  28. The Hitchhiker's Guide to the Galaxy: The 42nd Anniversary Edition of Douglas Adams's International Bestseller Douglas Adams
  29. Robot-Proof: Higher Education in the Age of Artificial Intelligence Joseph E. Aoun
  30. Graph Data Science with Python and Neo4j Timothy Eastridge
  31. Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again Eric Topol
  32. Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications Shreyas Subramanian
  33. Enhancing Deep Learning Performance Using Displaced Rectifier Linear Unit David Macêdo
  34. Power and Prediction: The Disruptive Economics of Artificial Intelligence Joshua Gans
  35. The Joy of X: A Guided Tour of Math, from One to Infinity Steven Strogatz
  36. The AI Delusion Gary Smith
  37. Data Science for Beginners: Comprehensive Guide to Most Important Basics in Data Science Alex Campbell
  38. Infinite Powers: How Calculus Reveals the Secrets of the Universe Steven Strogatz
  39. Fluke: The Math and Myth of Coincidence Joseph Mazur
  40. Data Mesh: Delivering Data-Driven Value at Scale Zhamak Dehghani
  41. Robot Rules: Regulating Artificial Intelligence Jacob Turner
  42. Chance in Biology: Using Probability to Explore Nature Mark Denny
  43. Theory of Games and Economic Behavior: 60th Anniversary Commemorative Edition John von Neumann
  44. Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World Mo Gawdat
  45. It All Adds Up: The Story of People and Mathematics Mickael Launay
  46. Coding for Beginners Using Python: A HANDS-ON, PROJECT-BASED INTRODUCTION TO LEARN CODING WITH PYTHON MARK MATTHES AND ERIC LUTZ

Elige el plan:

  • Más de 650.000 títulos

  • Kids mode

  • Modo sin conexión

  • Cancela cuando quieras

¡Más popular!
Oferta por tiempo limitado

Unlimited

Nada mejor que un audiolibro para esta temporada.

8.99 € /mes
Ahorra 34%
  • 1 cuenta

  • Acceso ilimitado

  • Escucha y lee los títulos que quieras

  • Modo sin conexión + Kids Mode

  • Cancela en cualquier momento

Suscríbete ahora

Family

Para los que quieren compartir historias con su familia y amigos.

Desde 15.99 €/mes
  • 2-3 cuentas

  • Acceso ilimitado

  • Escucha y lee los títulos que quieras

  • Modo sin conexión + Kids Mode

  • Cancela en cualquier momento

2 cuentas

15.99 € /mes
Pruébalo ahora