| Bibliography | Bock, Pascal: Development of a benchmarking framework for Inverse Reinforcement Learning algorithms based on Tetris.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Student Thesis No. 2464 (2015).
38 pages, english.
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| CR-Schema | D.2.8 (Software Engineering Metrics)
H.5.2 (Information Interfaces and Presentation User Interfaces)
I.2.1 (Applications and Expert Systems)
I.2.6 (Artificial Intelligence Learning)
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| Abstract | Tetris is one of the oldest, most popular and most well-known video games. The simple rules and scoring options make it a viable choice for benchmarking artificial intelligence, especially in the machine learning department. This work describes a customizable benchmarking frame- work using a simplified variant of the original Tetris game focused on Inverse Reinforcement Learning algorithms.
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Full text and
other links | PDF (243333 Bytes)
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| Department(s) | University of Stuttgart, Institute of Parallel and Distributed Systems, Machine Learning und Robotics
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| Superviser(s) | Toussaint, Marc |
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| Entry date | April 20, 2015 |
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