What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Surgery
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.
His most cited work include:
- Policy Gradient Methods for Reinforcement Learning with Function Approximation (3144 citations)
- Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning (1998 citations)
- Learning to act using real-time dynamic programming (969 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (20.46%)
- Reinforcement learning (18.01%)
- Internal medicine (9.22%)
What were the highlights of his more recent work (between 2015-2021)?
- Reinforcement learning (18.01%)
- Artificial intelligence (20.46%)
- Internal medicine (9.22%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- Control of memory, active perception, and action in minecraft (144 citations)
- Value Prediction Network (109 citations)
- Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning (68 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Surgery
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
