Deep Reinforcement Learning Hands-On
This book is a developer-oriented introduction to deep reinforcement learning (RL). Explore RL concepts and discover how you can solve complex and challenging problems using deep learning. Apply deep RL methods to train your agent to beat arcade games and board games and navigate real-world environments, including the stock market.
Offered by
Difficulty Level
Intermediate
Completion Time
18h12m
Language
English
About Book
Who Is This Book For?
Some fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.
Deep Reinforcement Learning Hands-On
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 18h12m 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
DQN Extensions
Stocks Trading Using RL
Policy Gradients – An Alternative
The Actor-Critic Method
Asynchronous Advantage Actor-Critic
Chatbots Training with RL
Web Navigation
Continuous Action Space
Trust Regions – TRPO, PPO, and ACKTR
Black-Box Optimization in RL
Beyond Model-Free – Imagination
AlphaGo Zero
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