Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On. - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
26
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27
Index

CartPole variance

To check this theoretical conclusion in practice, let's plot our policy gradient variance during the training for both the baseline version and the version without the baseline. The complete example is in Chapter12/01_cartpole_pg.py, and most of the code is the same as in Chapter 11, Policy Gradients – an Alternative. The differences in this version are the following:

  • It now accepts the command-line option --baseline, which enables the mean subtraction from the reward. By default, no baseline is used.
  • On every training loop, we gather the gradients from the policy loss and use this data to calculate the variance.

To gather only the gradients from the policy loss and exclude the gradients from the entropy bonus added for exploration, we need to calculate the gradients in two stages. Luckily, PyTorch allows this to be done easily. In the following code, only the relevant part of the training loop is included to illustrate the idea:

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