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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

Distributional policy gradients

As the last method of this chapter, we'll take a look at the very recent paper by Gabriel Barth-Maron, Matthew W. Hoffman, and others, called Distributional Policy Gradients, published in 2018. At the time of writing, this paper hasn't been uploaded to ArXiV yet, as it was only submitted for a review for the conference ICLR 2018. It is available at https://openreview.net/forum?id=SyZipzbCb.

The full name of the method is Distributed Distributional Deep Deterministic Policy Gradients or D4PG for short. The authors proposed several improvements to the DDPG method we've just seen to improve stability, convergence, and sample efficiency.

First of all, they adapted the distributional representation of the Q-value proposed ...

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Publisher Resources

ISBN: 9781788834247Supplemental Content