Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (1)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (1)
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)
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Index

The computation graph in PyTorch

Our first examples won't be around speeding up the baseline, but will show one common, and not always obvious, situation that can cost you performance. In Chapter 3, Deep Learning with PyTorch, we discussed the way PyTorch calculates gradients: it builds the graph of all operations that you perform on tensors, and when you call the backward() method of the final loss, all gradients in the model parameters are automatically calculated.

This works well, but RL code is normally much more complex than traditional supervised learning models, so the RL model that we are currently training is also being applied to get the actions that the agent needs to perform in the environment. The target network discussed in Chapter 6 makes it even more tricky. So, in DQN, a neural network (NN) is normally used in three different situations:

  1. When we want to calculate Q-values predicted by the network to get the loss in respect to reference Q-values approximated...