What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Reinforcement learning
His scientific interests lie mostly in Reinforcement learning, Artificial intelligence, Artificial neural network, Domain and Human–computer interaction.
His work on Reinforcement learning algorithm as part of general Reinforcement learning research is frequently linked to Set, thereby connecting diverse disciplines of science.
His Artificial intelligence research includes elements of Machine learning and Computer Go.
His work carried out in the field of Domain brings together such families of science as Network architecture, Q-learning, Control and Function approximation.
His Human–computer interaction study incorporates themes from Learning methods and Relevance.
His research in Learning environment tackles topics such as Bellman equation which are related to areas like Value.
His most cited work include:
- Human-level control through deep reinforcement learning (11046 citations)
- Mastering the game of Go with deep neural networks and tree search (7255 citations)
- Playing Atari with Deep Reinforcement Learning (4302 citations)
What are the main themes of his work throughout his whole career to date?
David Silver focuses on Artificial intelligence, Reinforcement learning, Machine learning, Artificial neural network and Bellman equation.
David Silver mostly deals with Mobile robot in his studies of Artificial intelligence.
David Silver has researched Reinforcement learning in several fields, including Domain, Human–computer interaction, Learning environment, Monte Carlo tree search and Bayesian probability.
His studies in Machine learning integrate themes in fields like Control and Bayes' theorem.
His work on Supervised learning as part of general Artificial neural network study is frequently linked to Process, bridging the gap between disciplines.
His Bellman equation study combines topics from a wide range of disciplines, such as Range, Dynamic programming and Representation.
He most often published in these fields:
- Artificial intelligence (66.15%)
- Reinforcement learning (53.12%)
- Machine learning (21.88%)
What were the highlights of his more recent work (between 2019-2021)?
- Reinforcement learning (53.12%)
- Artificial intelligence (66.15%)
- Artificial neural network (17.19%)
In recent papers he was focusing on the following fields of study:
Reinforcement learning, Artificial intelligence, Artificial neural network, Machine learning and Set are his primary areas of study.
He incorporates Reinforcement learning and Scalability in his research.
David Silver has researched Artificial intelligence in several fields, including Tree, Function and Bellman equation.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Learning progress, Decomposition and Measure.
He combines subjects such as Data point and Model learning with his study of Machine learning.
His Algorithm study incorporates themes from Domain and Function.
Between 2019 and 2021, his most popular works were:
- Improved protein structure prediction using potentials from deep learning (518 citations)
- Mastering Atari, Go, chess and shogi by planning with a learned model (54 citations)
- Behaviour Suite for Reinforcement Learning (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Reinforcement learning, Artificial intelligence, Algorithm, Function and Deep learning.
His work carried out in the field of Reinforcement learning brings together such families of science as Software engineering and Leverage.
David Silver studies Range which is a part of Artificial intelligence.
The concepts of his Algorithm study are interwoven with issues in Value, Bootstrapping and Domain.
He has included themes like Learning environment, Gradient descent and Hyperparameter in his Function study.
His Deep learning research includes themes of Exploit and Decision problem.
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