In this chapter, we discussed why it is important for PG methods to gather training data from multiple environments, due to their on-policy nature. We also implemented two different approaches to A3C, in order to parallelize and stabilize the training process. Parallelization will rise once again in this book, when we discuss black-box methods (Chapter 16, Black-Box Optimization in RL). In the upcoming chapters, we'll take a look at practical problems that could be solved using PG methods, which will wrap up the PG part of the book.
Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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Overview of this book
Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.
The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
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Current Title:
Deep Reinforcement Learning Hands-On
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
Free Chapter
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
Index
Customer Reviews