This paper experimentally evaluates multiagent learning algorithms playing repeated matrix games to maximize their cumulative return. Previous works assessed that Qlearning surpas...
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship l...
Different formal learning models address different aspects of learning. Below we compare learning via queries—interpreting learning as a one-shot process in which the learner i...
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...
In the context of the emerging paradigm of Lifelong Learning, competence-based learning is gradually attracting the attention of the Technology-Enhanced Learning community, since ...