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)
Other Books You May Enjoy

Multiprocessing in Python

Python includes the multiprocessing (most of the time abbreviated to just mp) module to support process-level parallelism and the required communication primitives. In our example, we will use the two main classes from this module:

  • mp.Queue: A concurrent multi-producer, multi-consumer FIFO (first in, first out) queue with transparent serialization and deserialization of objects placed in the queue
  • mp.Process: A piece of code that is run in the child process and methods to control it from the parent process

PyTorch provides its own thin wrapper around the multiprocessing module, which adds the proper handling of tensors and variables on CUDA devices and shared memory. It provides exactly the same functionality as the multiprocessing module from the standard library, so all you need to do is use import torch.multiprocessing instead of import multiprocessing.