# The Technical Activity Blogs

## Connectionists [NIPS 2006] Call for Posters & Participation for the Workshop Towards a New Reinforcement Learning?

============================================== NIPS 2006 Workshop CALL FOR POSTERS & PARTICIPATION Towards a New Reinforcement Learning? http://www.jan-peters.net/Research/NIPS2006 Whistler, CANADA: December 8, 2006 ============================================== Abstract ======= 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 net- works. 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 learn- ing. 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 con- nections 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 statisti- cal 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. Format ====== Our goal is to bring together researchers who have worked on reinforcement learning techniques which are heading towards new approaches in terms of bringing other statistical learning tech- niques to bear on RL. The workshop will consist of short presentations, posters, and panel discus- sions. Topics to be addressed include, but are not limited to: • Which methods from supervised and unsupervised learning are the most promising to help developing new RL approaches? • How can modern probabilistic and Bayesian method be beneficial for Reinforcement Learn- ing? • Which approaches can help reducing the number of open parameters in Reinforcement Learning? • Can the Reinforcement Learning Problem be reduced to Classification or Regression? • Can reinforcement learning be seen as a big filtering or prediction problem where the pre- diction of good actions is the main objective? • Are there useful alternative ways to formulate the RL problem? E.g, as a dynamic Bayesian network, by using multiplicative rewards, etc. • Can reinforcement learning be accelerated by incorporating biases, expert data from demon- stration, prior knowledge on reward functions, etc.? Invited Talks (tentative) =================== Game theoretic learning and planning algorithms, Geoff Gordon Reductive Reinforcement Learning, John Langford The Importance of Measure in Reinforcement Learning, Sham Kakade Sample Complexity Results for Reinforcement Learning in Large State Spaces, Csaba Szepesvari Policies Based on Trajectory Libraries, Martin Stolle Towards Bayesian Reinforcement Learning, Pascal Poupart Bayesian Policy Gradient Algorithms, Mohammed Ghavamsadeh Bayesian RL for Partially Observable Domains, Joelle Pinau Bayesian Reinforcement Learning with Gaussian Processes, Yaakov Engel From Imitation Learning to Reinforcement Learning, Nathan Ratliff Graphical Models for Imitation: A New Approach to Speeding up RL, Deepak Verma Apprenticeship learning and robotic control, Andrew Ng Variational Methods for Stochastic Optimization: A Unification of Population-Based Methods, Mark Andrews Probabilistic inference for solving structured MDPs and POMDPs, Marc Toussaint Poster Submission Instructions ======================== If you would like to present a poster at this workshop, please send an email to Jan Peters (jrpeters@usc.edu) no later than 13th November, 2006, specifying: -> Title -> Presenter and affiliation -> A short abstract with one or two references We intend to create an edited book with contributions of people who have presented at our workshop. We would be delighted if you would indicate whether you are interested to add a chapter/section to such a book? Dates & Deadlines for Poster Submissions ================================= November 13: Abstract Submission November 15: Acceptance Notification Organizing Committee ===================== Jan Peters University of Southern California Drew Bagnell Carnegie Mellon University Stefan Schaal University of Southern California