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Found 5 papers, 1 papers with code
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation
SCONE-GAN presents an end-to-end image translation, which is shown to be effective for learning to generate realistic and diverse scenery images.
SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning
Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems.
Model-based Reinforcement Learning
reinforcement-learning
+1
Informing a BDI Player Model for an Interactive Narrative
We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model.
Exploring Apprenticeship Learning for Player Modelling in Interactive Narratives
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.
Approximating Optimisation Solutions for Travelling Officer Problem with Customised Deep Learning Network
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 its variants are not commonly solved using such techniques.
