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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Things to try

Here is a list of things you can do to improve your understanding of the topic:

  1. In the D4PG code, I used a simple replay buffer, which was enough to get good improvement over DDPG. You can try to switch the example to the prioritized replay buffer in the same way as we did in Chapter 8, DQN Extensions, and check the effect.
  2. There are lots of interesting and challenging environments around. For example, you can start with other PyBullet environments, but there is also the DeepMind Control Suite (Tassa, Yuval, et al., DeepMind Control Suite, arXiv abs/1801.00690 (2018)), MuJoCo-based environments in Gym, and many others.
  3. You can request the trial license of MuJoCo and compare its stability, performance, and resulting policy with PyBullet.
  4. You can play with the very challenging Learning to Run competition from NIPS-2017 (which also took place in 2018 and 2019 with more challenging problems), where you are given a simulator of the human body and your agent...