<p> Action selection for an autonomous agent was studied within the confines of truck task scheduling. An experimental setup was established to compare a naive selection approach, a simple market-based optimization approach, and a learning-assisted market-based optimization over a series of scenarios with varying complexity. For sufficiently complex scenarios, the results showed that learning was able to improve the performance of the truck by delaying delivery to a given site until it was the most protable action available. This research adds to the existing autonomous planning research by demonstrating a novel approach for planning under resource constraints. This approach improves upon an existing market-based optimization technique through the use of on-line reinforcement learning for market adjustment.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1557547 |
| Date | 25 July 2014 |
| Creators | Danna, Russell J. |
| Publisher | University of Louisiana at Lafayette |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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