Towards Corrective Deep Imitation Learning in Data Intensive Environments: Helping robots to learn faster by leveraging human knowledge

Interactive imitation learning refers to learning methods where a human teacher interacts with an agent during the learning process providing feedback to improve its behaviour. This type of learning may be preferable with respect to reinforcement learning techniques when dealing with real-world problems. This fact is especially true in the case...

Conversation Quality: Modeling in Free-Standing Conversational Groups

Social interactions in general are multifaceted and there exists an wide set of factors and events that influence them. Hence, interactions as a social phenomena have been studied by researchers in the fields of psychology and social signal processing from different stand points. The common trend in literature is to perform an in-depth study on...

Digitalization of Retailing with Machine Learning: A Service-Product Design for Digital Merchandisers to Implement Machine Learning

The thesis addresses the implementation challenges of Machine Learning (ML) for merchandisers in the scenario of digitalization of retailing, and proposes a product-service design as the solution. The digitalization of retailing is defined as an on-going process to integrate Internet-connected digital technologies into interfaces between...

Obstacle avoidance for quadrotors using reinforcement learning and obstacle-airflow interactions

This research investigates and proposes a new method for obstacle detection and avoidance on quadrotors. One that does not require the addition of any sensors, but relies solely on measurements from the accelerometer and rotor controllers. The detection of obstacles is based on the principle that the airflow around a quadrotor changes when the...

Deep Reinforcement Learning with Feedback-based Exploration

Deep Reinforcement Learning enables us to control increasingly complex and high-dimensional problems. Modelling and control design is longer required, which paves the way to numerous in- novations, such as optimal control of evermore sophisticated robotic systems, fast and efficient scheduling and logistics, effective personal drug dosing...

Policy Learning with Human Teachers: Using directive feedback in a Gaussian framework

A prevalent approach for learning a control policy in the model-free domain is by engaging Reinforcement Learning (RL). A well known disadvantage of RL is the necessity for extensive amounts of data for a suitable control policy. For systems that concern physical application, acquiring this vast amount of data might take an extraordinary amount...