What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Reinforcement learning, Machine learning, Video game and Representation.
His Artificial intelligence research is multidisciplinary, relying on both Pattern recognition and Bellman equation.
His Reinforcement learning algorithm study in the realm of Reinforcement learning interacts with subjects such as Zero.
His Machine learning study incorporates themes from Scalability, Theoretical computer science, Message passing, Data mining and Bayesian probability.
His Unsupervised learning research includes elements of Artificial neural network, Supervised learning and Monte Carlo tree search, Computer Go.
His research in Computer Go focuses on subjects like General video game playing, which are connected to Search algorithm.
His most cited work include:
- Mastering the game of Go with deep neural networks and tree search (7255 citations)
- Mastering the game of Go without human knowledge (3646 citations)
- Private traits and attributes are predictable from digital records of human behavior (1407 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Reinforcement learning, Machine learning, Algorithm and Data mining.
Much of his study explores Artificial intelligence relationship to Pattern recognition.
In his study, Incentive is inextricably linked to Social dilemma, which falls within the broad field of Reinforcement learning.
His research in Machine learning intersects with topics in Probabilistic logic and Inference.
His Algorithm course of study focuses on Mathematical optimization and Gradient descent.
The various areas that Thore Graepel examines in his Data mining study include Probability distribution and Message passing.
He most often published in these fields:
- Artificial intelligence (39.81%)
- Reinforcement learning (18.48%)
- Machine learning (18.48%)
What were the highlights of his more recent work (between 2018-2021)?
- Reinforcement learning (18.48%)
- Artificial intelligence (39.81%)
- Human–computer interaction (7.11%)
In recent papers he was focusing on the following fields of study:
Thore Graepel spends much of his time researching Reinforcement learning, Artificial intelligence, Human–computer interaction, Game theory and Nash equilibrium.
Thore Graepel combines subjects such as Cooperative game theory, Communication, Cognitive psychology and Social dilemma with his study of Reinforcement learning.
His work carried out in the field of Artificial intelligence brings together such families of science as Class and Video game.
His Game theory study combines topics in areas such as Counterfactual thinking and Analytics.
His Nash equilibrium research incorporates elements of Artificial neural network, Solver, Equilibrium selection and Oracle.
Thore Graepel focuses mostly in the field of Artificial neural network, narrowing it down to matters related to Scalability and, in some cases, Principal component analysis.
Between 2018 and 2021, his most popular works were:
- Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model (179 citations)
- Human-level performance in 3D multiplayer games with population-based reinforcement learning (167 citations)
- Emergent Coordination through Competition (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Reinforcement learning, Human–computer interaction, Artificial intelligence, Video game and Order.
He has researched Reinforcement learning in several fields, including Artificial general intelligence and Microeconomics.
The concepts of his Human–computer interaction study are interwoven with issues in Scheme, Control, Competition and Game theoretic.
His research investigates the connection with Artificial intelligence and areas like Bellman equation which intersect with concerns in State.
Thore Graepel carries out multidisciplinary research, doing studies in Video game and Population based.
Throughout his Order studies, Thore Graepel incorporates elements of other sciences such as SIMPLE, Exploration problem, Development, Theoretical computer science and Resource allocation.
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