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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My congratulations, you've made another step towards understanding modern, state-of-the-art RL methods! We learned about some very important concepts that are widely used in deep RL: the value of state, the value of actions, and the Bellman equation in various forms. We saw the value iteration method, which is a very important building block in the area of Q-learning. Finally, we got to know how value iteration can improve our FrozenLake solution.

In the next chapter, we'll learn about deep Q-networks, which started the deep RL revolution in 2013, by beating humans on lots of Atari 2600 games.