Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

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.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
Contributors
Preface
Other Books You May Enjoy
Index

A3C – data parallelism


The first version of A3C parallelization that we'll check (which was outlined on Figure 2) has both one main process which carries out training and several children processes communicating with environments and gathering experience to train on. For simplicity and efficiency, the neural network (NN) weights broadcasting from the trainer process is not implemented. Instead of explicitly gathering and sending weights to children, the network is shared between all processes using PyTorch built-in capabilities, allowing us to use the same nn.Module instance with all its weights in different processes by calling the share_memory() method on NN creation. Under the hood, this method has zero overhead for CUDA (as GPU memory is shared among all host's processes) or shared memory IPC in the case of CPU computation. In both cases, the method improves performance, but limits our example for one single machine using one single GPU card for training and data gathering. It's not...