Interactive verification of game design and playing strategies

Reinforcement learning is considered as one of the most suitable and prominent methods for solving game problems due to its capability to discover good strategies by extended se self-training and limited initial knowledge. In this paper we elaborate on using reinforcement learning for verifying game designs and playing strategies. Specifically, we examine a new strategy game that has been trained on self-playing games and analyze the game performance after human interaction. We demonstrate, through selected game instances, the impact of human interference to the learning process, and eventually the game design.