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Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
4.3 (38)
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Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On

4.3 (38)
By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks. With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field. In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.
Table of Contents (28 chapters)
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26
Other Books You May Enjoy
27
Index

Models tested on data

Once we've got our models ready, we can check them against our dataset and free-form sentences. During the training, both training tools (train_crossent.py and train_scst.py) periodically save the model, which is done in two different situations: when the BLEU score on the test dataset updates the maximum and every 10 epochs. Both kinds of models have the same format (produced by the torch.save() method) and contain the model's weights. Except the weights, I save the token to integer ID mapping, which will be used by tools to preprocess the phrases.

To experiment with models, two utilities exist: data_test.py and use_model.py. data_test.py loads the model, applies it to all phrases from the given genre, and reports the average BLEU score. Before the testing, phrase pairs are grouped by the first phrase. For example, the following is the result for two models, trained on the comedy genre. The first one was trained by the cross-entropy method and the...

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