Skip navigation
Skip navigation

Policy Gradient Methods: Variance Reduction and Stochastic Convergence

Greensmith, Evan

Description

In a reinforcement learning task an agent must learn a policy for performing actions so as to perform well in a given environment. Policy gradient methods consider a parameterized class of policies, and using a policy from the class, and a trajectory through the environment taken by the agent using this policy, estimate the performance of the policy with respect to the parameters. Policy gradient methods avoid some of the problems of value function methods, such as policy degradation,...[Show more]

CollectionsOpen Access Theses
Date published: 2005-03
Type: Thesis (PhD)
URI: http://hdl.handle.net/1885/47105
http://digitalcollections.anu.edu.au/handle/1885/47105

Download

File Description SizeFormat Image
02whole.pdf1.19 MBAdobe PDFThumbnail
01front.pdf69.02 kBAdobe PDFThumbnail


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  23 August 2018/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator