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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Why a continuous space?

All the examples that we've seen so far in the book had a discrete action space, so you might have the wrong impression that discrete actions dominate the field. This is a very biased view, of course, and just reflects the selection of domains that we picked our test problems from. Besides Atari games and simple, classical RL problems, there are lots of tasks requiring more than just making a selection from a small and discrete set of things to do.

To give you an example, just imagine a simple robot with only one controllable joint, which can be rotated in some range of degrees. Usually, to control a physical joint, you have to specify either the desired position or the force applied. In both cases, you need to make a decision about a continuous value. This value is fundamentally different from a discrete action space, as the set of values that you can make a decision on is potentially infinite. For instance, you can ask the joint to move to a 13.5° angle or 13.512...