In this talk I will present an overview of some of the past and current lines of research in reinforcement learning (RL), as well as some of the challenges that research in this area has faced in the last decades. I will describe a range of recent results that may bring significant advances on some of these fundamental research challenges, and yet rely on the “simplest” optimization approach – gradient search. The ultimate goal of this talk is to provide a high-level perception of RL while hint on current active avenues of research in this area.
Gradient Approaches to Reinforcement Learning
May 25, 2010
1:00 pm
Francisco Melo
Francisco S. Melo received his PhD in Electrical and Computer Engineering at Instituto Superior Técnico, in Lisbon, Portugal. During 2007 he held an appointment as a short-term researcher in the Computer Vision Lab, at the Institute for Systems and Robotics (Lisbon, Portugal) and in 2008 he joined the Computer Science Department of Carnegie Mellon University as a Post-Doctoral Fellow. Since June 2009 he is a Researcher at the Intelligent Agents and Synthetic Characters Group of INESC-ID, where he develops research within reinforcement learning, planning under uncertainty, multiagent and multi-robot systems, developmental robotics, and sensor networks.INESC-IDSeminários
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