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 quickly skimmed through a very interesting domain of continuous control, using RL methods and checked three different algorithms on one problem of a four-legged robot. In our training, we used an emulator, but there are real models of this robot made by the Ghost Robotics company (you can check out the cool video on YouTube:

We applied three training methods to this environment: A2C, DDPG, and D4PG (which has shown the best results). In the next chapter, we'll continue exploring the continuous action domain and will check a different set of improvements: trust region.