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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A tensor is the fundamental building block of all DL toolkits. The name sounds cool and mystic, but the underlying idea is that a tensor is a multi-dimensional array. One single number is like a point, which is zero-dimensional, while a vector is one-dimensional like a line segment, and a matrix is a two-dimensional object. Three-dimensional number collections can be represented by a parallelepiped of numbers, but don't have a separate name in the same way as matrix. We can keep this term for collections of higher dimensions, which are named multi-dimensional matrices or tensors.

Figure 1: Going from a single number to an n-dimension tensor

Creation of tensors

If you're familiar with the NumPy library (and you should be), then you already know that its central purpose is the handling of multi-dimensional arrays in a generic way. In NumPy, such arrays aren't called tensors, but, in fact, they are tensors. Tensors are used very widely in scientific computations, as generic storage for...