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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

Double DQN

The next fruitful idea on how to improve a basic DQN came from DeepMind researchers in a paper titled Deep Reinforcement Learning with Double Q-Learning ([3] van Hasselt, Guez, and Silver, 2015). In the paper, the authors demonstrated that the basic DQN has a tendency to overestimate values for Q, which may be harmful to training performance and sometimes can lead to suboptimal policies. The root cause of this is the max operation in the Bellman equation, but the strict proof is too complicated to write down here. As a solution to this problem, the authors proposed modifying the Bellman update a bit.

In the basic DQN, our target value for Q looked like this:

Q(t+1, a) was Q-values calculated using our target network, so we update with ...

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Publisher Resources

ISBN: 9781788834247Supplemental Content