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Python Deep Learning - Second Edition
book

Python Deep Learning - Second Edition

by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
January 2019
Intermediate to advanced content levelIntermediate to advanced
386 pages
11h 13m
English
Packt Publishing
Content preview from Python Deep Learning - Second Edition

Policy gradient methods

All RL algorithms we discussed until now have tried to learn the state- or action-value functions. For example, in Q-learning we usually follow an ε-greedy policy, which has no parameters (OK, it has one parameter) and relies on the value function instead. In this section, we'll discuss something new: how to approximate the policy itself with the help of policy gradient methods. We'll follow a similar approach as in Chapter 8, Reinforcement Learning Theory, in the Value function approximation section.

There, we introduced a value approximation function, which is described by a set of parameters w (neural net weights). Here, we'll introduce a parameterized policy , which is described by a set of parameters θ. As with ...

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Publisher Resources

ISBN: 9781789348460Supplemental Content