Towards a New Reinforcement Learning?
Jan Peters · Stefan Schaal · Drew Bagnell

Fri Dec 8th 12:00 -- 12:00 AM @ Sutcliffe B
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During the last decade, many areas of statistical machine learning have reached a high level of maturity with novel, efficient, and theoretically well founded algorithms that increasingly removed the need for heuristics and manual parameter tuning, which dominated the early days of neural networks. Reinforcement learning (RL) has also made major progress in theory and algorithms, but is somehow lagging behind the success stories of classification, supervised, and unsupervised learning. Besides the long-standing question for scalability of RL to larger and real world problems, even in simpler scenarios, a significant amount of manual tuning and human insight is needed to achieve good performance, e.g., as in exemplified in issues like eligibility factors, learning rates, the choice of function approximators and their basis functions for policy and/or value functions, etc. Some of the reasons for the progress of other statistical learning disciplines comes from connections to well- established fundamental learning approaches, like maximum-likelihood with EM, Bayesian statistics, linear regression, linear and quadratic programming, graph theory, function space analysis, etc. Therefore, the main question of this workshop is to discuss, how other statistical learning techniques may be used to developed new RL approaches in order to achieve properties including higher numerical robustness, easier use in terms of open parameters, probabilistic and Bayesian interpretations, better scalability, the inclusions of prior knowledge, etc.

Author Information

Jan Peters (TU Darmstadt & MPI Intelligent Systems)

Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universitaet Darmstadt and at the same time a senior research scientist and group leader at the Max-Planck Institute for Intelligent Systems, where he heads the interdepartmental Robot Learning Group. Jan Peters has received the Dick Volz Best 2007 US PhD Thesis Runner-Up Award, the Robotics: Science & Systems - Early Career Spotlight, the INNS Young Investigator Award, and the IEEE Robotics & Automation Society‘s Early Career Award as well as numerous best paper awards. In 2015, he was awarded an ERC Starting Grant. Jan Peters has studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich and FernUni Hagen in Germany, at the National University of Singapore (NUS) and the University of Southern California (USC). He has received four Master‘s degrees in these disciplines as well as a Computer Science PhD from USC.

Stefan Schaal (MPI-IS and USC)
Drew Bagnell (Carnegie Mellon University)

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