Lifelong Reinforcement Learning On Mobile Robots

Machine learning has shown tremendous growth in the past decades, unlocking new capabilities in a variety of fields including computer vision, natural language processing, and robotic control. While the sophistication of individual problems a learning system can handle has greatly advanced, the ability of a system to extend beyond an individual problem to adapt and solve new problems has progressed more slowly. This thesis explores the problem of progressive learning. The goal is to develop methodologies that accumulate, transfer, and adapt knowledge in applied settings where the system is faced with the ambiguity and resource limitations of operating in the physical world. There are undoubtedly many challenges to designing such a system, my thesis looks at the component of this problem related to how knowledge from previous tasks can be a benefit in the domain of reinforcement learning where the agent receives rewards for positive actions. Reinforcement learning is particularly difficult when training on physical systems, like mobile robots, where repeated trials can damage the system and unrestricted exploration is often associated with safety risks. I investigate how knowledge can be efficiently accumulated and applied to future reinforcement learning problems on mobile robots in order to reduce sample complexity and enable systems to adapt to novel settings. Doing this involves mathematical models which can combine knowledge from multiple tasks, methods for restructuring optimizations and data collection to handle sequential updates, and data selection strategies that can be used to address resource limitations

Toward an Integrated Competence-based System Supporting Lifelong Learning and Employability: Concepts, Model, and Challenges

Miao, Y., Van der Klink, M., Boon, J., Sloep, P. B., & Koper, R. (2009). Toward an Integrated Competence-based System Supporting Lifelong Learning and Employability: Concepts, Model, and Challenges. In M. Spaniol, Q. Li, R. Klamma & R. W. H. Lau (Eds.), Proceedings of the 8th International Conference Advances in Web Based Learning - ICWL 2009 (pp. 265-276). August, 19-21, 2009, Aachen, Germany. Lecture Notes in Computer Science 5686; Berlin, Heidelberg: Springer-Verlag.Efficient and effective lifelong learning requires that people can make informed decisions about their continuous personal development in the different stages of their lives. In this paper we state that lifelong learners need to be characterized as decision-makers. In order to improve the quality of their decisions we propose the development of an integrated lifelong learning and employment support system, which traces learners’ competence development and provides a decision support environment. An abstract conceptual model has been developed and the main design ideas have been documented using Z notation. Moreover, we analyzed the main technical challenges for the realization of the target system: competence information fusion, decision analysis models, spatial indexing structures and browsing structures and visualization of competence related information objects.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org