Abstract
This paper presents a study on the robustness and variability of performance of general video game-playing agents. Agents analyzed includes those that won the different legs of the 2014 and 2015 General Video Game AI Competitions, and two sample agents distributed with its framework. Initially, these agents are run in four games and ranked according to the rules of the competition. Then, different modifications to the reward signal of the games are proposed and noise is introduced in either the actions executed by the controller, their forward model, or both. Results show that it is possible to produce a significant change in the rankings by introducing the modifications proposed here. This is an important result because it enables the set of human-authored games to be automatically expanded by adding parameter-varied versions that add information and insight into the relative strengths of the agents under test. Results also show that some controllers perform well under almost all conditions, a testament to the robustness of the GVGAI benchmark.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 |
| Publisher | IEEE Computer Society |
| ISBN (Electronic) | 9781509018833 |
| DOIs | |
| State | Published - Feb 21 2017 |
| Event | 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 - Santorini, Greece Duration: Sep 20 2016 → Sep 23 2016 |
Other
| Other | 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 |
|---|---|
| Country | Greece |
| City | Santorini |
| Period | 9/20/16 → 9/23/16 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Software
Cite this
Analyzing the robustness of general video game playing agents. / Perez-Liebana, Diego; Samothrakis, Spyridon; Togelius, Julian; Schaul, Tom; Lucas, Simon M.
2016 IEEE Conference on Computational Intelligence and Games, CIG 2016. IEEE Computer Society, 2017. 7860430.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Analyzing the robustness of general video game playing agents
AU - Perez-Liebana, Diego
AU - Samothrakis, Spyridon
AU - Togelius, Julian
AU - Schaul, Tom
AU - Lucas, Simon M.
PY - 2017/2/21
Y1 - 2017/2/21
N2 - This paper presents a study on the robustness and variability of performance of general video game-playing agents. Agents analyzed includes those that won the different legs of the 2014 and 2015 General Video Game AI Competitions, and two sample agents distributed with its framework. Initially, these agents are run in four games and ranked according to the rules of the competition. Then, different modifications to the reward signal of the games are proposed and noise is introduced in either the actions executed by the controller, their forward model, or both. Results show that it is possible to produce a significant change in the rankings by introducing the modifications proposed here. This is an important result because it enables the set of human-authored games to be automatically expanded by adding parameter-varied versions that add information and insight into the relative strengths of the agents under test. Results also show that some controllers perform well under almost all conditions, a testament to the robustness of the GVGAI benchmark.
AB - This paper presents a study on the robustness and variability of performance of general video game-playing agents. Agents analyzed includes those that won the different legs of the 2014 and 2015 General Video Game AI Competitions, and two sample agents distributed with its framework. Initially, these agents are run in four games and ranked according to the rules of the competition. Then, different modifications to the reward signal of the games are proposed and noise is introduced in either the actions executed by the controller, their forward model, or both. Results show that it is possible to produce a significant change in the rankings by introducing the modifications proposed here. This is an important result because it enables the set of human-authored games to be automatically expanded by adding parameter-varied versions that add information and insight into the relative strengths of the agents under test. Results also show that some controllers perform well under almost all conditions, a testament to the robustness of the GVGAI benchmark.
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U2 - 10.1109/CIG.2016.7860430
DO - 10.1109/CIG.2016.7860430
M3 - Conference contribution
BT - 2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
PB - IEEE Computer Society
ER -