AI lectures: Learning for Autonomous Mobile Systems

Frans Oliehoek - Associate Professor TU Delft, is a guest of Gijs Dubbelman

Title | Exploiting structure in learning and planning

Abstract

Reinforcement learning (RL) and more generally sequential decision-making deal with problems where the decision maker ('agent') needs to take actions over time. While impressive results have been achieved on challenging domains like Atari, Go, and Starcraft, most of this work relies on neural networks to form their own internal abstractions. However, in many applications, we may be able to exploit some knowledge about the structure of the environment to guide this process.  In this talk I will cover some of my work that tries to exploit structure to define effective methods for planning and reinforcement learning.

 

Bio

Dr. Frans A. Oliehoek is Associate Professor at Delft University of Technology. He received his Ph.D. in Computer Science (2010) from the University of Amsterdam (UvA), and held positions at various universities including MIT, Maastricht University and the University of Liverpool.  Frans' research interests lie in the intersection of machine learning, AI and game theory. He is considered an expert in the field of decision making under uncertainty, with emphasis on multiagent systems. He organized several workshops and symposia on topics such as Multiagent Sequential Decision Making Under Uncertainty, Challenges and Opportunities in Multiagent Learning, and Learning, Inference and Control of Multi-Agent System. He received the best PC-member award at AAMAS 2012, and was awarded a number of research grants, including a prestigious ERC Starting Grant for his project “INFLUENCE: Influence-based Decision-making in Uncertain Environments”.