Book
Deep Reinforcement Learning Hands-On
With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perform a range of practical tasks.
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Difficulty Level
Intermediate
Completion Time
27h32m
Language
English
About Book
Who Is This Book For?
Some fluency in Python is assumed. Sound understanding of the fundamentals of deep learning will be helpful. This book is an introduction to deep RL and requires no background in RL
Deep Reinforcement Learning Hands-On
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 27h32m total length
What Is Reinforcement Learning?
OpenAI Gym
Deep Learning with PyTorch
The Cross-Entropy Method
Tabular Learning and the Bellman Equation
Deep Q-Networks
Higher-Level RL libraries
DQN Extensions
Ways to Speed up RL
Stocks Trading Using RL
Policy Gradients – an Alternative
The Actor-Critic Method
Asynchronous Advantage Actor-Critic
Training Chatbots with RL
The TextWorld environment
Web Navigation
Continuous Action Space
RL in Robotics
Trust Regions – PPO, TRPO, ACKTR, and SAC
Black-Box Optimization in RL
Advanced exploration
Beyond Model-Free – Imagination
AlphaGo Zero
RL in Discrete Optimisation
Multi-agent RL
Related Resources
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