Autonomous systems rely on learning from experience to automatically refine their strategy and adapt to their environment, and thereby have huge advantages over traditional hand engineered systems. At PROWLER.io we use reinforcement learning (RL) for sequential decision making under uncertainty to develop intelligent agents capable of acting in dynamic and unknown environments. In this talk we first give a general overview of the goals and the research conducted at PROWLER.io. Then, we will talk about two specific research topics. The first is Information-Theoretic Model Uncertainty which deals with the problem of making robust decisions that take into account unspecified models of the environment. The second is Deep Model-Based Reinforcement Learning which deals with the problem of learning the transition and the reward function of a Markov Decision Process in order to use it for data-efficient learning.
Biography: Felix Leibfried and Jordi Grau-Moya were former PhD Students at the Max Planck Institute for Intelligent Systems and Biological Cybernetics in Tuebingen from 2012 to 2017, studying decision-making with limited computational resources and under model uncertainty. During their PhD they conducted research internships at Microsoft Research, Cambridge UK, and at Mitsubishi Research Electronic Laboratories Cambridge MA, respectively. Since April 2017 they are Machine Learning Researchers at PROWLER.io, a start-up for autonomous decision-making located in Cambridge, UK.