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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In this chapter, we saw an alternative way of solving RL problems: PG, which is different in many ways from the familiar DQN method. We explored the basic method called REINFORCE, which is a generalization of our first method in RL-domain cross entropy. This method is simple, but, being applied to the Pong environment, didn’t show good results.

In the next chapter, we’ll consider ways to improve the stability of PG by combining both families of value-based and policy-based methods.