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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

Baseline DQN

In this problem, the major challenge lies in inconvenient observation and action spaces. Text sequences might be problematic on their own, as we discussed in the previous chapter. The variability of sequence lengths might cause vanishing and exploding gradients in RNNs, slow training, and convergence issues. In addition to that, our TextWorld environment provides us with several such sequences that we need to handle separately. Our scene description string, for example, might have a completely different meaning to the agent than the inventory string, which describes our possessions.

As mentioned, another obstacle is the action space. As you have seen in the previous section, TextWorld might provide us with a list of commands that we can execute in every state. It significantly reduces the action space we need to choose from, but there are other complications. One of them is that the list of admissible commands changes from state to state (as different locations might...

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