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

Combining everything

You have now seen all the DQN improvements mentioned in the paper Rainbow: Combining Improvements in Deep Reinforcement Learning, but it was done in an incremental way, which helped you to understand the idea and implementation of every improvement. The main point of the paper was to combine those improvements and check the results. In the final example, I've decided to exclude categorical DQN and double DQN from the final system, as they haven't shown too much improvement on our guinea pig environment. If you want, you can add them and try using a different game. The complete example is available in Chapter08/08_dqn_rainbow.py.

First of all, we need to define our network architecture and the methods that have contributed to it:

  • Dueling DQN: our network will have two separate paths for the value of the state distribution and advantage distribution. On the output, both paths will be summed together, providing the final value probability distributions...