Techniques for batch reinforcement learning in robotics

This thesis addresses the Batch Reinforcement Learning methods in Robotics. This sub-class of Reinforcement Learning has shown promising results and has been the focus of recent research. Three contributions are proposed that aim to extend the state-of-art methods allowing for a faster and more stable learning process, such as required for learning in Robotics. The Q-learning update-rule is widely applied, since it allows to learn without the presence of a model of the environment. However, this update-rule is transition-based and does not take advantage of the underlying episodic structure of collected batch of interactions. The Q-Batch update-rule is proposed in this thesis, to process experiencies along the trajectories collected in the interaction phase. This allows a faster propagation of obtained rewards and penalties, resulting in faster and more robust learning. Non-parametric function approximations are explored, such as Gaussian Processes. This type of approximators allows to encode prior knowledge about the latent function, in the form of kernels, providing a higher level of exibility and accuracy. The application of Gaussian Processes in Batch Reinforcement Learning presented a higher performance in learning tasks than other function approximations used in the literature. Lastly, in order to extract more information from the experiences collected by the agent, model-learning techniques are incorporated to learn the system dynamics. In this way, it is possible to augment the set of collected experiences with experiences generated through planning using the learned models. Experiments were carried out mainly in simulation, with some tests carried out in a physical robotic platform. The obtained results show that the proposed approaches are able to outperform the classical Fitted Q Iteration.

English for tourism : contribution to language use through mobile learning

Tourism is growing and is becoming more competitive. Destinations need to find elements which demonstrate their uniqueness, the singularity which allows them to differentiate themselves from others. This struggle for uniqueness makes economies become more competitive and competition is a central element in the dynamics of Tourism. Technology is also an added value for tourism competitiveness, as it allows destinations to become internationalised and known worldwide. In this scenario, research has increased as a means to study Tourism trends in fields such as sociology and marketing. Nevertheless, there are areas in which there is not much research done and which are fundamental: these are the areas concerned with identities, communication and interpersonal relations. In this regard, Linguistics has a major role for different reasons: firstly, it studies language itself and through it, communication, secondly, language conveys culture and, thirdly, it is by enriching language users that innovation in Tourism and in knowledge, as a whole, is made possible. This innovation, on the other hand, has repercussions in areas such as management, internationalisation and marketing as well. It is, therefore, the objective of this thesis to report on how learning experiences take place in Tourism undergraduate English language classes as well as to give an account of enhanced results in classes where mobile learning was adopted. In this way, an alliance between practice and research was established. This is beneficial for the teaching and learning process because by establishing links between research based insight and practice, the outcome is grounded knowledge which helps make solid educational decisions. This research, therefore, allows to better understand if learners accept working with mobile technologies in their learning process. Before introducing any teaching and learning approach, it was necessary to be informed, as well, of how English for tourism programmes are organised. This thesis also illustrates through the premises of Systemic Functional Linguistics that language use can be enhanced by using mobile technology in Tourism undergraduate language classes.