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 one of the most widely used methods in deep RL: A2C, which wisely combines the PG update with value of the state approximation. We introduced the idea behind A2C by analyzing the effect of the baseline on the statistics and convergence of gradients. Then we checked the extension of the baseline idea: A2C, where a separate network head provides us with the baseline for the current state.

In the next chapter, we will look at ways to perform the same algorithm in a distributed way.