Playing Tetris Game with Reinforcement Learning

This paper is implemented the architecture for solving the Tetris game which are defined as a complex problem in general through reinforcement learning. Tetris games require the actor's quick judgment ability and speed of response because the blocks must be stacked in an optimal location quickly, taking into account the shape and rotation of randomly appearing blocks. Also, since the number of cases is very large due to the various block types and order, if the subject of performance is a person, there is a limit to performance by simply relying on memory and memorization. therefore, the reinforcement learning architecture implemented in this study is applied not only to the implementation of the learning model but also We apply the Heuristic to increase the decision accuracy as the weighting method of reward. As a result, we were able to obtain high scores. Although it is not yet possible to say that he has completely conquered the tetris game, In several experiments, reinforcement learning was able to play better than some people. However, we also identified the disadvantage that heuristics are more influential on performance than learning models. In this paper, the structure of these architectures and the techniques and algorithms used are described in detail and the direction of approach is given.