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Deep Reinforcement Learning Hands-On
book

Deep Reinforcement Learning Hands-On

by Oleg Vasilev, Maxim Lapan, Martijn van Otterlo, Mikhail Yurushkin, Basem O. F. Alijla
June 2018
Intermediate to advanced content levelIntermediate to advanced
546 pages
13h 30m
English
Packt Publishing
Content preview from Deep Reinforcement Learning Hands-On

Value iteration in practice

The complete example is in Chapter05/01_frozenlake_v_learning.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've seen it. For example, if in state 0 we execute action 1 ten times, after three times it leads us to state 4 and after seven times to state 5. Entry with the key (0, 1) in this table will be a dict {4: 3, 5: 7}. We use this table to estimate ...
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Publisher Resources

ISBN: 9781788834247Supplemental Content