Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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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 code and the new concepts...