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

Training both tigers and deer

The next example is the scenario when both tigers and deer are controlled by different DQN models being trained simultaneously. Tigers are rewarded for living longer, which means eating more deer, as at every step in the simulation they lose health points. Deer are also rewarded on every timestamp.

The code is in Chapter25/ and it is quite a simple extension of the previous example. For both groups of agents, we have a separate Agent class instance, which communicates with the environment. As the observation for both groups is different, we have two separate networks, replay buffers, and experience sources. On every training step, we sample batches from both replay buffers and then train both networks independently.

I'm not going to put the code here, as it differs from the previous example only in small details. If you are curious, you can check GitHub examples. The following are plots with the convergence results.