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IntroductionThe term serious game (SG) origins from [1] and refers to games not exclusively developed for mere entertainment, but primarily for creating educational value. SGs are counted among the current e-learning trends and are gaining more and more acceptance and influence [2]. SG development slightly differs from that of entertainment games: SGs are often individual products for a restricted target audience, industrial branch or company. This results in high expenditure and deficient reusability, although the market shows growing interest in cost-efficient and customized applications [2]. One of the most relevant, but also most time-consuming tasks is the creation of reasonable, human-like non-player character (NPC) behaviour [3][4][5]. Thus, simplifying authoring and adaptation of AI in SGs seems desirable and profitable.Machine learning, especially reinforcement learning (RL), is occasionally used for automated NPC behaviour generation. Nevertheless, several issues arise when applying RL to create believable and diverse behaviour in complex scenarios. We indicate a way to overcome these issues by combining RL with human guidance, including effective collaboration between a learning system and human experts.In this paper, we give a short introduction to some challenges of behaviour generation in serious games and to the background of RL, deep reinforcement learning (DRL) and interactive reinforcement learning (iRL). We show related approaches and depict current issues of applying DRL methods to SGs. Furthermore, we introduce SanTrain as a SG providing challenging scenarios for NPC behaviour generation. Finally, we show how our approach of interactive deep reinforcement learning (iDRL), integrated into a flexible framework, could enhance AI development in SGs and exemplarily indicate valuable application opportunities in SanTrain.

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