Deep Reinforcement Learning with Python
Deep Reinforcement Learning with Python - Second Edition will help you learn reinforcement learning algorithms, techniques and architectures – including deep reinforcement learning – from scratch. This new edition is an extensive update of the original, reflecting the state-of-the-art latest thinking in reinforcement learning.
Offered by
Difficulty Level
Intermediate
Completion Time
25h20m
Language
English
About Book
Who Is This Book For?
If you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.
Deep Reinforcement Learning with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 25h20m total length
Fundamentals of Reinforcement Learning
A Guide to the Gym Toolkit
The Bellman Equation and Dynamic Programming
Monte Carlo Methods
Understanding Temporal Difference Learning
Case Study – The MAB Problem
Deep Learning Foundations
A Primer on TensorFlow
Deep Q Network and Its Variants
Policy Gradient Method
Actor-Critic Methods – A2C and A3C
Learning DDPG, TD3, and SAC
TRPO, PPO, and ACKTR Methods
Distributional Reinforcement Learning
Imitation Learning and Inverse RL
Deep Reinforcement Learning with Stable Baselines
Reinforcement Learning Frontiers
Appendix 1 – Reinforcement Learning Algorithms
Appendix 2 – Assessments
Related Resources
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