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Deep Reinforcement Learning Hands-On. - Second Edition

You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Pages 826 pages

Edition 2nd Edition

Languages

Concepts

Deep Reinforcement Learning

Author (1):

Maxim Lapan

Table of Contents (28) Chapters

Preface

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 Optimization

Multi-agent RL

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Index

DDPG training and results

To train the policy using our model, we will use deep deterministic policy gradients (DDPGs), which we covered in detail in Chapter 17, Continuous Action Space. I won't spend time here showing the code, which is in Chapter18/train_ddpg.py and Chapter18/lib/ddpg.py. For exploration, the Ornstein-Uhlenbeck process was used in the same way as for the Minitaur model.

The only thing I'd like to emphasize is the size of the model, in which the actor part was intentionally reduced to meet our hardware limitations. The actor has one hidden layer with 20 neurons, giving just two matrices (not counting the bias) of 28×20 and 20×4. The input dimensionality is 28, due to observation stacking, where four past observations are passed to the model. This dimensionality reduction leads to very fast training, which can be done without a GPU involved.

To train the model, you should run the train_ddpg.py program, which accepts the following arguments...

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Authors (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.

See other products by Maxim Lapan

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