Exploring Apprenticeship Learning for Player Modelling in Interactive Narratives

TL;DR

This paper explores using Apprenticeship Learning with Receding Horizon IRL to model and imitate player behavior in interactive narratives, aiming to improve personalization and understanding of player goals.

Contribution

It introduces an early application of Apprenticeship Learning to simulate player behavior in interactive fiction, demonstrating the potential of RHIRL for goal inference and behavior replication.

Findings

RHIRL can learn action sequences to complete a game

The approach provides insights into generating player-specific behavior

Preliminary results show promise for player modeling in INs

Abstract

In this paper we present an early Apprenticeship Learning approach to mimic the behaviour of different players in a short adaption of the interactive fiction Anchorhead. Our motivation is the need to understand and simulate player behaviour to create systems to aid the design and personalisation of Interactive Narratives (INs). INs are partially observable for the players and their goals are dynamic as a result. We used Receding Horizon IRL (RHIRL) to learn players' goals in the form of reward functions, and derive policies to imitate their behaviour. Our preliminary results suggest that RHIRL is able to learn action sequences to complete a game, and provided insights towards generating behaviour more similar to specific players.