Deep Reinforcement Learning in Python

Series
Addison-Wesley
Author
Laura Harding Graesser / Keng Wah Loon  
Publisher
Addison-Wesley
Cover
Softcover
Edition
1
Language
English
Total pages
360
Pub.-date
January 2019
ISBN13
9780135172384
ISBN
0135172381
Related Titles


Product detail

Product Price CHF Available  
9780135172384
Deep Reinforcement Learning in Python
56.90 approx. 7-9 days

Description

In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence. 

Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes: 
  • Components of an RL system, including environment and agents
  • Value-based algorithms: SARSA, Q-learning and extensions, offline learning
  • Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques
  • Combined methods: Actor-Critic and extensions; scalability through async methods
  • Agent evaluation
  • Advanced and experimental techniques, and more
  • How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning 
  • Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise 
  • Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms
  • Includes case studies, practical tips, definitions, and other aids to learning and mastery
  • Prepares readers for exciting future advances in artificial general intelligence
The accessible, hands-on, full-color tutorial for building practical deep reinforcement learning solutions
  • How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning 
  • Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise 
  • Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms
  • Includes case studies, practical tips, definitions, and other aids to learning and mastery
  • Prepares readers for exciting future advances in artificial general intelligence

 

Author

Laura Graesser enjoys experimenting with, and writing about, machine learning techniques. Currently she is studying for an MS in Computer Science. She is particularly interested in deep learning algorithms and their application to reinforcement learning, computer vision, and NLP. Most recently, she is interested in combining reinforcement learning with supervised learning, knowledge distillation, and in tackling multi-modal and multi-task learning.

Wah Loon Keng likes building softwares for the research and application of theories in Computer Science and AI. He is an active open source contributor, and the creator of the data science platform at Eligible Inc. As a student, he did research on quantum foundation, computer science and mathematics. He is always interested in the theories of intelligence, especially reinforcement learning, semantics, and intuitive theories of mind. With his engineering skills, he is building experiment frameworks to test these theories, and OpenAI Lab is one.