2012 - Fellow of Alfred P. Sloan Foundation
His main research concerns Reinforcement learning, Artificial intelligence, Markov decision process, Mathematical optimization and Machine learning. The Q-learning research Satinder Singh does as part of his general Reinforcement learning study is frequently linked to other disciplines of science, such as Function, therefore creating a link between diverse domains of science. His Artificial intelligence research includes elements of Domain, Predictive state representation and Set.
His research in Markov decision process focuses on subjects like Object, which are connected to Variety, Action, Hierarchy and Interface. The various areas that he examines in his Mathematical optimization study include Partially observable Markov decision process, Stochastic game, Markov process and Markov chain. His study on Transfer of learning is often connected to Sketch as part of broader study in Machine learning.
Satinder Singh spends much of his time researching Artificial intelligence, Reinforcement learning, Internal medicine, Mathematical optimization and Cardiology. His work carried out in the field of Artificial intelligence brings together such families of science as Action, Domain, Machine learning and Set. His study in Reinforcement learning is interdisciplinary in nature, drawing from both Algorithm, Set and Human–computer interaction.
His study brings together the fields of Markov decision process and Mathematical optimization. Partially observable Markov decision process is the focus of his Markov decision process research.
Reinforcement learning, Artificial intelligence, Internal medicine, Mathematical optimization and Surgery are his primary areas of study. His biological study spans a wide range of topics, including Set, Human–computer interaction, Set, Value and Generalization. Set is closely attributed to Algorithm in his work.
His Artificial neural network study in the realm of Artificial intelligence interacts with subjects such as Simple. The study of Internal medicine is intertwined with the study of Cardiology in a number of ways. His research on Mathematical optimization frequently links to adjacent areas such as Markov decision process.
Satinder Singh mostly deals with Reinforcement learning, Artificial intelligence, Machine learning, Contrast and Algorithm. Satinder Singh integrates several fields in his works, including Reinforcement learning and Learning environment. His studies deal with areas such as Baseline and Adaptation as well as Artificial intelligence.
His Machine learning study combines topics from a wide range of disciplines, such as Epilepsy, Seizure detection, Seizure types, Generalizability theory and Multi-task learning. Satinder Singh works mostly in the field of Contrast, limiting it down to topics relating to Set and, in certain cases, Control, Submodular set function, Greedy algorithm, Logarithm and Maximization. His research investigates the connection between Algorithm and topics such as Set that intersect with problems in Model selection, Exponential function, Focus and Polynomial.
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Policy Gradient Methods for Reinforcement Learning with Function Approximation
Richard S Sutton;David A. McAllester;Satinder P. Singh;Yishay Mansour.
neural information processing systems (1999)
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Richard S. Sutton;Doina Precup;Satinder Singh.
Artificial Intelligence (1999)
Learning to act using real-time dynamic programming
Andrew G. Barto;Steven J. Bradtke;Satinder P. Singh.
Artificial Intelligence (1995)
Near-Optimal Reinforcement Learning in Polynomial Time
Michael Kearns;Satinder Singh.
Machine Learning (2002)
The national lung screening trial: Overview and study design
Constantine A. Gatsonis;Denise R. Aberle;Christine D. Berg;William C. Black.
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Tommi Jaakkola;Michael I. Jordan;Satinder P. Singh.
neural information processing systems (1993)
Reinforcement learning with replacing eligibility traces
Satinder P. Singh;Richard S. Sutton.
Machine Learning (1996)
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms
Satinder Singh;Tommi Jaakkola;Michael L. Littman;Csaba Szepesvári.
Machine Learning (2000)
Intrinsically Motivated Reinforcement Learning
Nuttapong Chentanez;Andrew G. Barto;Satinder P. Singh.
neural information processing systems (2004)
Action-conditional video prediction using deep networks in Atari games
Junhyuk Oh;Xiaoxiao Guo;Honglak Lee;Richard Lewis.
neural information processing systems (2015)
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