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 walked through and implemented lots of DQN improvements discovered by researchers since the first DQN paper was published in 2015. This list is far from complete. First of all, for the list of methods, I've used the paper, [1] Rainbow: Combining Improvements in Deep Reinforcement Learning, which was published by DeepMind, so the list of methods is definitely biased to DeepMind papers. Secondly, RL is so active nowadays that new papers come out almost every day, which makes it very hard to keep up with, even if we limit ourselves to one kind of RL model such as a DQN. The goal of this chapter was to give you a practical view of different ideas that the field has developed.

In the next chapter, we will apply our DQN knowledge to a real-life scenario of stocks trading.