Date of Award

12-2009

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Engineering

Major Professor

Itamar Arel

Committee Members

Gregory Peterson, Hairong Qi

Abstract

Vision-based machine learning agents are tasked with making decisions based on high-dimensional, noisy input, placing a heavy load on available resources. Moreover, observations typically provide only partial information with respect to the environment state, necessitating robust state inference by the agent. Reinforcement learning provides a framework for decision making with the goal of maximizing long-term reward. This thesis introduces a novel approach to vision-based reinforce- ment learning through the use of a consolidated actor-critic model (CACM). The approach takes advantage of artificial neural networks as non-linear function approximators and the reduced com- putational requirements of the CACM scheme to yield a scalable vision-based control system. In this thesis, a comparison between the actor-critic and CACM is made. Additionally, the affect of observation prediction and correlated exploration has on the agent's performance is investigated.

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