Theory and Application of Reward Shaping in Reinforcement Learning
Laud, Adam Daniel
This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/81640
Description
Title
Theory and Application of Reward Shaping in Reinforcement Learning
Author(s)
Laud, Adam Daniel
Issue Date
2004
Doctoral Committee Chair(s)
Gerald DeJong
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Computer Science
Language
eng
Abstract
We demonstrate our theory with two applications: a stochastic gridworld, and a bipedal walking control task. In all cases, the experiments uphold the analytical predictions; most notably that reducing the reward horizon implies faster learning. The bipedal walking task demonstrates that our reward shaping techniques allow a conventional reinforcement learning algorithm to find a good behavior efficiently despite a large state space with stochastic actions.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
