Testing match-3 video games with Deep Reinforcement Learning
This work addresses the need to reduce human resource costs in video game testing for software houses, though it is incremental as it applies an existing DRL method to a new domain.
The authors tackled the problem of automating testing for match-3 video games by using Deep Reinforcement Learning, specifically a Dueling Deep Q-Network, and found that it achieved a higher success rate compared to random players and performed similarly to real users in most cases.
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.