Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
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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Training: SCST

As we've already discussed, RL training methods applied to the seq2seq problem can potentially improve the final model. The main reasons are:

  • Better handling of multiple target sequences. For example, hi could be replied with hi, hello, not interested, or something else. The RL point of view is to treat our decoder as a process of selecting actions when every action is a token to be generated, which fits better to the problem.
  • Optimizing the BLEU score directly instead of cross-entropy loss. Using the BLEU score for the generated sequence as a gradient scale, we can push our model toward the successful sequences and decrease the probability of unsuccessful ones.
  • By repeating the decoding process, we can generate more episodes to train on, which will lead to better gradient estimation.
  • Additionally, using the self-critical sequence training approach, we can get the baseline almost for free, without increasing the complexity of our model, which...