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

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

Training: SCST

As we've already discussed, RL training methods applied to the seq2seq problem can potentially improve the final model. The main reasons are:

Better handling of multiple target sequences. For example, hi could be replied with hi, hello, not interested, or something else. The RL point of view is to treat our decoder as a process of selecting actions when every action is a token to be generated, which fits better to the problem.
Optimizing the BLEU score directly instead of cross-entropy loss. Using the BLEU score for the generated sequence as a gradient scale, we can push our model toward the successful sequences and decrease the probability of unsuccessful ones.
By repeating the decoding process, we can generate more episodes to train on, which will lead to better gradient estimation.
Additionally, using the self-critical sequence training approach, we can get the baseline almost for free, without increasing the complexity of our model, which...