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- W3040482971 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." @default.
- W3040482971 created "2020-07-10" @default.
- W3040482971 creator A5078980569 @default.
- W3040482971 date "2020-06-30" @default.
- W3040482971 modified "2023-09-27" @default.
- W3040482971 title "Testing match-3 video games with Deep Reinforcement Learning" @default.
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