9 citations · 9 across the 3 of their papers we have counts for
4 papers
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning
Andrew Chester, Michael Dann, Fabio Zambetta +1
Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many inte…
Informing a BDI Player Model for an Interactive Narrative
Jessica Rivera-Villicana, Fabio Zambetta, James Harland +1
This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge abou…
Exploring Apprenticeship Learning for Player Modelling in Interactive Narratives
Jessica Rivera-Villicana, Fabio Zambetta, James Harland +1
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 motiva…
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
Wei Shao, Flora D. Salim, Jeffrey Chan +2
Deep learning has been extended to a number of new domains with critical success, though some traditional orienteering problems such as the Travelling Salesman Problem (TSP) and it…