Interactive Narrative Personalization with Deep Reinforcement Learning

Interactive Narrative Personalization with Deep Reinforcement Learning

Pengcheng Wang, Jonathan Rowe, Wookhee Min, Bradford Mott, James Lester

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 3852-3858. https://doi.org/10.24963/ijcai.2017/538

Data-driven techniques for interactive narrative generation are the subject of growing interest. Reinforcement learning (RL) offers significant potential for devising data-driven interactive narrative generators that tailor players’ story experiences by inducing policies from player interaction logs. A key open question in RL-based interactive narrative generation is how to model complex player interaction patterns to learn effective policies. In this paper we present a deep RL-based interactive narrative generation framework that leverages synthetic data produced by a bipartite simulated player model. Specifically, the framework involves training a set of Q-networks to control adaptable narrative event sequences with long short-term memory network-based simulated players. We investigate the deep RL framework’s performance with an educational interactive narrative, Crystal Island. Results suggest that the deep RL-based narrative generation framework yields effective personalized interactive narratives.
Keywords:
Multidisciplinary Topics and Applications: Computer Games
Multidisciplinary Topics and Applications: Interactive Entertainment