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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

ES on HalfCheetah

In the next example, we'll go beyond the simplest ES implementation and look at how this method can be parallelized efficiently using the shared seed strategy proposed by the paper [1]. To show this approach, we'll use the environment from the roboschool library that we already experimented with in Chapter 15, Trust Regions – TRPO, PPO, and ACKTR, HalfCheetah, which is a continuous action problem where a weird two-legged creature gains reward by running forward without injuring itself.

First, let's discuss the idea of shared seeds. The performance of the ES algorithm is mostly determined by the speed that we can gather our training batch, which consists of sampling the noise and checking the total reward of the perturbed noise. ...

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Publisher Resources

ISBN: 9781788834247Supplemental Content