Hunter-Prey or Prey-Pursuit problem is a common toy domain for Reinforcement Learning, but the size of the state space is exponential in the parameters such as size of the grid or number of agents. As the size of the state space makes the flat Q-learning impossible to use for different scenarios, this thesis presents an approach to make the size of the state space constant by producing agents that use previously learned knowledge to perform on bigger scenarios containing more agents. Inspired from HRL methods, the method is composed of a parallel subtasks schema dividing the task into choices of simpler subtasks, a state representation technique convenient for this schema and its extension for bigger grids. Experimental results show that proposed method successfully provides agents that perform near to hand-coded agents by using constant sized state space independent from parameters of the domain.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12610596/index.pdf |
| Date | 01 June 2009 |
| Creators | Iscen, Atil |
| Contributors | Polat, Faruk |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
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