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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

Categorical DQN

The last and the most complicated method in our DQN improvements toolbox is from the very recent paper published by DeepMind in June 2017 called A Distributional Perspective on Reinforcement Learning ([9] Bellemare, Dabney and Munos 2017).

In the paper, the authors questioned the fundamental piece of Q-learning: Q-values and tried to replace them with more generic Q-value probability distribution. Let's try to understand the idea. Both the Q-learning and value iteration methods are working with the values of actions or states represented as simple numbers and showing how much total reward we can achieve from state or action. However, is it practical to squeeze all future possible reward into one number? In complicated environments, ...

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Publisher Resources

ISBN: 9781788834247Supplemental Content