Dear colleagues, On June 25, the Developing Minds global online lecture series is proud to host Andrew Barto, Prof. Emeritus of Univ. of Massachusetts Amherst, USA, speaking on „Rediscovering Reinforcement Learning“. His talk will take place at: 10:00 am EDT (Eastern Daylight Time, USA) 14:00 UTC (Universal Coordinated Time) 16:00 CEST (Central European Summer Time) 23:00 JST (Japan Standard Time). The talk will be hosted via zoom: https://uni-frankfurt.zoom-x.de/j/62410045708?pwd=EmEDiJtB0NZ0XPbSBngt6sbx2a... Meeting-ID: 624 1004 5708 Kenncode: 883974 Abstract: The idea of reinforcement learning (RL) as a key principle of animal learning has been around at least since Edward Thorndike proposed the “Law of Effect” in 1898. Machine implementation of this principle began with electro-mechanical machines in the 1930s, and the earliest idea for computer implementation was probably Turing’s 1948 proposal of a computer implementation of a “pleasure-pain system”. In this talk I review what has followed Turing’s unimplemented proposal, starting with the first computer experiments in 1954, up to what we now know as modern computational RL. Short Bio: Andrew Barto is Professor Emeritus of Computer Science, University of Massachusetts Amherst, having retired in 2012. He served as Chair of the UMass Department of Computer Science from 2007 to 2011. He received a B.S. with distinction in mathematics from the University of Michigan in 1970, and a Ph.D. in Computer Science in 1975, also from the University of Michigan. He joined the Computer Science Department of the University of Massachusetts Amherst in 1977. Before retiring he co-directed the Autonomous Learning Laboratory at UMass Amherst, which produced many notable machine learning researchers. Professor Barto is a Fellow of the American Association for the Advancement of Science (AAAS) and a Fellow and Life Member of the IEEE. He received the 2004 IEEE Neural Network Society Pioneer Award for contributions to the field of reinforcement learning, the IJCAI-17 Award for Research Excellence for groundbreaking and impactful research in both the theory and application of reinforcement learning, and a University of Massachusetts Neurosciences Lifetime Achievement Award in 2019. Most recently, he and his former doctoral student, Richard Sutton, were co-recipients of the 2024 Association for Computing Machinery’s A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning, and he and Sutton received the 2026 IEEE Frank Rosenblatt Award for contributions to reinforcement learning and artificial intelligence. He has published over one hundred papers or chapters in journals, books, and conference and workshop proceedings. He is co-author with Sutton of "Reinforcement Learning: An Introduction," MIT Press, 1998. A much expanded second edition was published in 2018. The talk will be recorded and made available for later viewing. For more information on the talk series and recordings of previous events, please visit: https://sites.google.com/view/developing-minds-series/home Best regards, Jochen Triesch -- Prof. Dr. Jochen Triesch Johanna Quandt Chair for Theoretical Life Sciences Frankfurt Institute for Advanced Studies and Goethe University Frankfurt Recent publications: https://www.nature.com/articles/s41467-025-64234-z https://ieeexplore.ieee.org/abstract/document/11204381?casa_token=RBlsm2RTae...