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.

Master in Innovative Tourism Development: a blended learning experience in the Polytechnic Institute of Viana do Castelo

This chapter analyses a Portuguese HEI’s first experience with blended learning. Focusing on the Master in Innovative Tourism Development currently being held at the Superior School of Technology and Management of the Polytechnic Institute of Viana do Castelo, it presents the institutional background and rationale behind the course’s implementation, identifying its strengths and weaknesses, as well as areas of potential development. Based on an evaluation research carried out throughout the course’s first three editions and that involved students, faculty and institutional leaders, it summarizes the main results of a long term-study, putting forth a set of recommendations for improvements and defining potential areas of intervention in future editions of the course.