Abstract
A large number of methods are being developed in the deep reinforcement learning area recently, but the scope of their application is limited. The number of environments does not always allow for a comprehensive assessment of a new agent training algorithm. The main purpose of this article is to present another environment for Match-3 game that could be expanded, which would have a connection with the real business. The results for the most popular deep reinforcement learning algorithms are presented as a baseline.
I. Makarov—The work was supported by the Russian Science Foundation under grant 17-11-01294 and performed at National Research University Higher School of Economics, Russia.
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Detailed implementation can be found at https://github.com/kamildar/gym-match3.
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Kamaldinov, I., Makarov, I. (2019). Deep Reinforcement Learning Methods in Match-3 Game. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_5
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