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https://www.um.edu.mt/library/oar/handle/123456789/81916
Title: | PCGRL : procedural content generation via reinforcement learning |
Authors: | Khalifa, Ahmed Bontrager, Philip Earle, Sam Togelius, Julian |
Keywords: | Computer games -- Design Level design (Computer science) Machine learning Artificial intelligence |
Issue Date: | 2020 |
Publisher: | Association for the Advancement of Artificial Intelligence (AAAI) |
Citation: | Khalifa, A., Bontrager, P., Earle, S., & Julian, T. (2020). PCGRL : procedural content generation via reinforcement learning. Proceedings of the Sixteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20), 16(1), 95-101. |
Abstract: | We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes, and apply these to three game environments. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/81916 |
ISBN: | 9781577358497 |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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PCGRL_procedural_content_generation_via_reinforcement_learning_2020.pdf Restricted Access | 2.16 MB | Adobe PDF | View/Open Request a copy |
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