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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Log-likelihood training

Imagine that we need to create a machine translation system from one language (say, French) into another language (English) using the seq2seq model. Let's assume that we have a good, large dataset of sample translations with French-English sentences that we're going to train our model on. How do we do this?

The encoding part is obvious: we just apply our encoder RNN to the first sentence in the training pair, which produces an encoded representation of the sentence. The obvious candidate for this representation will be the hidden state returned from the last RNN application. At encoding stage, we ignore the RNN's outputs, taking into account only the hidden state from the last RNN...