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)
26
Other Books You May Enjoy
27
Index

Value iteration in practice

The complete example is in Chapter05/01_frozenlake_v_iteration.py. The central data structures in this example are as follows:

  • Reward table: A dictionary with the composite key "source state" + "action" + "target state". The value is obtained from the immediate reward.
  • Transitions table: A dictionary keeping counters of the experienced transitions. The key is the composite "state" + "action", and the value is another dictionary that maps the target state into a count of times that we have seen it. For example, if in state 0 we execute action 1 ten times, after three times it will lead us to state 4 and after seven times to state 5.

    The entry with the key (0, 1) in this table will be a dict with contents {4: 3, 5: 7}. We can use this table to estimate the probabilities of our transitions.

  • Value table: A dictionary that maps a state into the calculated value of this state.

The overall...