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.
| Identifer | oai:union.ndltd.org:UTENN_/oai:trace.tennessee.edu:utk_gradthes-1582 |
| Date | 01 December 2009 |
| Creators | Niedzwiedz, Christopher Allen |
| Publisher | Trace: Tennessee Research and Creative Exchange |
| Source Sets | University of Tennessee Libraries |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses |
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