Interactive Narrative Personalization with Deep Reinforcement Learning
نویسندگان
چکیده
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.
منابع مشابه
Decomposing Drama Management in Educational Interactive Narrative: A Modular Reinforcement Learning Approach
Recent years have seen growing interest in data-driven approaches to personalized interactive narrative generation and drama management. Reinforcement learning (RL) shows particular promise for training policies to dynamically shape interactive narratives based on corpora of player-interaction data. An important open question is how to design reinforcement learning-based drama managers in order...
متن کاملOptimizing Player Experience in Interactive Narrative Planning: A Modular Reinforcement Learning Approach
Recent years have witnessed growing interest in data-driven approaches to interactive narrative planning and drama management. Reinforcement learning techniques show particular promise because they can automatically induce and refine models for tailoring game events by optimizing reward functions that explicitly encode interactive narrative experiences’ quality. Due to the inherently subjective...
متن کاملOptimizing Tutorial Planning in Educational Games: A Modular Reinforcement Learning Approach
Recent years have seen a growing interest in educational games, which integrate the engaging features of digital games with the personalized learning functionalities of intelligent tutoring systems. A key challenge in creating educational games, particularly those supported with interactive narrative, is devising narrativecentered tutorial planners, which dynamically adapt gameplay events to in...
متن کاملA Modular Reinforcement Learning Framework for Interactive Narrative Planning
A key functionality provided by interactive narrative systems is narrative adaptation: tailoring story experiences in response to users’ actions and needs. We present a datadriven framework for dynamically tailoring events in interactive narratives using modular reinforcement learning. The framework involves decomposing an interactive narrative into multiple concurrent sub-problems, formalized ...
متن کاملImproving Student Problem Solving in Narrative-Centered Learning Environments: a Modular Reinforcement Learning Framework
Narrative-centered learning environments comprise a class of gamebased learning environments that embed problem solving in interactive stories. A key challenge posed by narrative-centered learning is dynamically tailoring story events to enhance student learning. In this paper, we investigate the impact of a data-driven tutorial planner on students’ learning processes in a narrative-centered le...
متن کامل