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Reinforcement Learning with TensorFlow
book

Reinforcement Learning with TensorFlow

by Sayon Dutta
April 2018
Intermediate to advanced content levelIntermediate to advanced
334 pages
10h 18m
English
Packt Publishing
Content preview from Reinforcement Learning with TensorFlow

Why policy optimization methods?

In this section, we will cover the pros and cons of policy optimization methods over value-based methods. The advantages are as follows:

  • They provides better convergence.
  • They are highly effective in case of high-dimensional/continuous state-action spaces. If action spaces are very big then a max function in a value-based method will be computationally expensive. So, the policy-based method directly changes the policy by changing the parameters instead of solving the max function at each step.
  • Ability to learn stochastic policies.

The disadvantages associated with policy-based methods are as follows:

  • Converges to local instead of global optimum
  • Policy evaluation is inefficient and has high variance

We ...

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Publisher Resources

ISBN: 9781788835725Supplemental Content