Version 1.0
By Anthony Wertz
awertz@knights.ucf.edu
- Description
(Abstract taken from paper "report.pdf" in repository.)
Without using modified methods, the vanilla Q-learning algorithm is not capable of handling policy construction for even simple problems as soon as the state space becomes too large. This paper introduces a method of Q-learning policy generation for large state spaces and dynamic environments by proposing a simple state space reduction transformation model. The algorithm is based on the unmodified Q-learning implementation but designed such that it can seamlessly benefit from many already existing Q-learning improvements.
- LICENSE
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.