Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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

A3C with gradients parallelism

The next approach that we will consider to parallelize A2C implementation will have several child processes, but instead of feeding training data to the central training loop, they will calculate the gradients using their local training data, and send those gradients to the central master process.

This process is responsible for combining those gradients together (which is basically just summing them) and performing an SGD update on the shared network.

The difference might look minor, but this approach is much more scalable, especially if you have several powerful nodes with multiple GPUs connected with the network. In this case, the central process in the data-parallel model quickly becomes a bottleneck, as the loss calculation and backpropagation are computationally demanding. Gradient parallelization allows for the spreading of the load on several GPUs, performing only a relatively simple operation of gradient combination in the central place...