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You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook
Published inJan 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781838826994
Edition2nd Edition
Languages
Python
Tools
KerasTensorFlow
Concepts
Deep Reinforcement Learning
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Author (1)
Maxim Lapan
Maxim Lapan
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Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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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...
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Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994
© 2020 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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