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)
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Index

Double DQN

The next fruitful idea on how to improve a basic DQN came from DeepMind researchers in the paper titled Deep Reinforcement Learning with Double Q-Learning ([3] van Hasselt, Guez, and Silver, 2015). In the paper, the authors demonstrated that the basic DQN tends to overestimate values for Q, which may be harmful to training performance and sometimes can lead to suboptimal policies. The root cause of this is the max operation in the Bellman equation, but the strict proof is too complicated to write down here. As a solution to this problem, the authors proposed modifying the Bellman update a bit.

In the basic DQN, our target value for Q looked like this:

Q'(st+1, a) was Q-values calculated using our target network, so we update with the trained network every n steps. The authors of the paper proposed choosing actions for the next state using the trained network, but taking values of Q from the target network. So, the new expression for target Q-values will look...