Testing match-3 video games with Deep Reinforcement Learning
- URL: http://arxiv.org/abs/2007.01137v2
- Date: Tue, 24 Nov 2020 11:30:08 GMT
- Title: Testing match-3 video games with Deep Reinforcement Learning
- Authors: Nicholas Napolitano
- Abstract summary: We study the possibility to use the Deep Reinforcement Learning to automate the testing process in match-3 video games.
We test this kind of network on the Jelly Juice game, a match-3 video game developed by the redBit Games.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Testing a video game is a critical step for the production process and
requires a great effort in terms of time and resources spent. Some software
houses are trying to use the artificial intelligence to reduce the need of
human resources using systems able to replace a human agent. We study the
possibility to use the Deep Reinforcement Learning to automate the testing
process in match-3 video games and suggest to approach the problem in the
framework of a Dueling Deep Q-Network paradigm. We test this kind of network on
the Jelly Juice game, a match-3 video game developed by the redBit Games. The
network extracts the essential information from the game environment and infers
the next move. We compare the results with the random player performance,
finding that the network shows a highest success rate. The results are in most
cases similar with those obtained by real users, and the network also succeeds
in learning over time the different features that distinguish the game levels
and adapts its strategy to the increasing difficulties.
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