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You're reading from  Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook
Published inJan 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781838826994
Edition2nd Edition
Languages
Python
Tools
KerasTensorFlow
Concepts
Deep Reinforcement Learning
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Author (1)
Maxim Lapan
Maxim Lapan
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Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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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.

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Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994
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Author (1)

author image
Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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