You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781838826994

Length 826 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Chatbots

Author (1):

Maxim Lapan

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Table of Contents (28) Chapters

Preface

1. What Is Reinforcement Learning?

2. OpenAI Gym FREE CHAPTER

3. Deep Learning with PyTorch

4. The Cross-Entropy Method

5. Tabular Learning and the Bellman Equation

6. Deep Q-Networks

7. Higher-Level RL Libraries

8. DQN Extensions

9. Ways to Speed up RL

10. Stocks Trading Using RL

11. Policy Gradients – an Alternative

12. The Actor-Critic Method

13. Asynchronous Advantage Actor-Critic

14. Training Chatbots with RL

15. The TextWorld Environment

16. Web Navigation

17. Continuous Action Space

18. RL in Robotics

19. Trust Regions – PPO, TRPO, ACKTR, and SAC

20. Black-Box Optimization in RL

21. Advanced Exploration

22. Beyond Model-Free – Imagination

23. AlphaGo Zero

24. RL in Discrete Optimization

25. Multi-agent RL

26. Other Books You May Enjoy

27. Index

Policy experiments

The first model that I trained was with the Height objective and without zeroing the yaw component. A video of the robot executing the policy is available here: https://www.youtube.com/watch?v=u5rDogVYs9E. The movements are not very natural. In particular, the front-right leg is not moving at all. This model is available in the source tree as Chapter18/hw/libhw/t1.py.

As this might be related to the yaw observation component, which is different during the training and inference, the model was retrained with the --zero-yaw command-line option. The result is a bit better: all legs are now moving, but the robot's actions are still not very stable. The video is here: https://www.youtube.com/watch?v=1JVVnWNRi9k. The model used is in Chapter18/hw/libhw/t1zyh.py.

The third experiment was done with a different training objective, HeightOrient, which not only takes into account the height of the model, but also checks that the body of the robot is parallel to the...

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You're reading from Deep Reinforcement Learning Hands-On Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more

Table of Contents (28) Chapters

Policy experiments

Authors (1)

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Table of Contents (28) Chapters

Policy experiments

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