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Deep Reinforcement Learning Hands-On

You're reading from   Deep Reinforcement Learning Hands-On Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788834247
Length 546 pages
Edition 1st Edition
Languages
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Author (1):
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Maxim Lapan Maxim Lapan
Author Profile Icon Maxim Lapan
Maxim Lapan
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Table of Contents (21) Chapters Close

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. DQN Extensions 8. Stocks Trading Using RL 9. Policy Gradients – An Alternative 10. The Actor-Critic Method 11. Asynchronous Advantage Actor-Critic 12. Chatbots Training with RL 13. Web Navigation 14. Continuous Action Space 15. Trust Regions – TRPO, PPO, and ACKTR 16. Black-Box Optimization in RL 17. Beyond Model-Free – Imagination 18. AlphaGo Zero Other Books You May Enjoy Index

The chatbot example

In the beginning of this chapter, we talked a bit about chatbots and NLP, so let's try to implement something simple using seq2seq and RL training. In total, there are two large groups of chatbots distinguished: entertainment human-mimicking and goal-oriented chatbots. The first group is supposed to entertain a user giving human-like replies to a user's phrases, without fully understanding them. The latter category is much harder to implement and is supposed to solve a user's problem: provide information, change reservations or switch on and off your home toaster. Most of the latest efforts in the industry are focused on the goal-oriented group, but the problem is far from being fully solved yet. As this chapter is supposed to give a short example of the methods described, we'll focus on training an entertainment bot using an online dataset with phrases extracted from movies.

Despite the simplicity of this problem, this example is large in terms of...

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