D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 55 Citations 11,468 274 World Ranking 2875 National Ranking 173

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Shimon Whiteson mainly focuses on Reinforcement learning, Artificial intelligence, Machine learning, Artificial neural network and Set. His Reinforcement learning research is multidisciplinary, incorporating elements of Mathematical optimization and Bellman equation. His Mathematical optimization study combines topics from a wide range of disciplines, such as Markov decision process and Bayesian probability.

In the subject of general Artificial intelligence, his work in Active learning and Deep learning is often linked to Key, Block and Routing, thereby combining diverse domains of study. His study in Machine learning is interdisciplinary in nature, drawing from both Task and Adaptation. His studies in Artificial neural network integrate themes in fields like Genetic algorithm and Inductive transfer.

His most cited work include:

  • Learning to Communicate with Deep Multi−Agent Reinforcement Learning (405 citations)
  • Counterfactual Multi−Agent Policy Gradients (374 citations)
  • Learning to Communicate with Deep Multi-Agent Reinforcement Learning (281 citations)

What are the main themes of his work throughout his whole career to date?

Shimon Whiteson spends much of his time researching Reinforcement learning, Artificial intelligence, Machine learning, Mathematical optimization and Bellman equation. In his study, Shimon Whiteson carries out multidisciplinary Reinforcement learning and Set research. His work in Artificial neural network, Evolutionary computation, Temporal difference learning, Robot and Inference is related to Artificial intelligence.

Many of his research projects under Machine learning are closely connected to Meta learning and Action selection with Meta learning and Action selection, tying the diverse disciplines of science together. His research in the fields of Maximization overlaps with other disciplines such as Bounded function, Observable and Sample. His Maximization study combines topics in areas such as Partially observable Markov decision process and Submodular set function.

He most often published in these fields:

  • Reinforcement learning (53.60%)
  • Artificial intelligence (46.40%)
  • Machine learning (27.70%)

What were the highlights of his more recent work (between 2019-2021)?

  • Reinforcement learning (53.60%)
  • Artificial intelligence (46.40%)
  • Machine learning (27.70%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Reinforcement learning, Artificial intelligence, Machine learning, Bellman equation and Benchmark. His Reinforcement learning research is multidisciplinary, incorporating perspectives in Theoretical computer science, Mathematical optimization, Algorithm, Hyperparameter and Robustness. His Artificial neural network, Inference and Supervised learning study in the realm of Artificial intelligence interacts with subjects such as Generalization and Set.

His Value study in the realm of Machine learning connects with subjects such as Action selection, Scheme, Meta learning and Interface. His Bellman equation research is multidisciplinary, relying on both Factorization, Partially observable Markov decision process, Function approximation and Applied mathematics. His Benchmark study incorporates themes from Range and Facial feedback hypothesis, Facial expression.

Between 2019 and 2021, his most popular works were:

  • Deep Coordination Graphs (15 citations)
  • VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (15 citations)
  • Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (14 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

His primary scientific interests are in Reinforcement learning, Artificial intelligence, Bellman equation, Monotonic function and Benchmark. You can notice a mix of various disciplines of study, such as Micromanagement, Space, Set, Meaningful learning and Value, in his Reinforcement learning studies. His Artificial intelligence study often links to related topics such as Machine learning.

In Bellman equation, Shimon Whiteson works on issues like Applied mathematics, which are connected to Factorization, Function approximation and Temporal difference learning. His studies deal with areas such as Range and Control, Robotic control as well as Benchmark. His Artificial neural network study combines topics from a wide range of disciplines, such as Knowledge transfer and Pseudocount.

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.

Best Publications

Learning to Communicate with Deep Multi−Agent Reinforcement Learning

Jakob Foerster;Ioannis Alexandros Assael;Nando de Freitas;Shimon Whiteson.
neural information processing systems (2016)

879 Citations

Learning to Communicate with Deep Multi−Agent Reinforcement Learning

Jakob Foerster;Ioannis Alexandros Assael;Nando de Freitas;Shimon Whiteson.
neural information processing systems (2016)

879 Citations

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster;Yannis M. Assael;Nando de Freitas;Shimon Whiteson.
arXiv: Artificial Intelligence (2016)

806 Citations

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

Jakob N. Foerster;Yannis M. Assael;Nando de Freitas;Shimon Whiteson.
arXiv: Artificial Intelligence (2016)

806 Citations

Counterfactual Multi−Agent Policy Gradients

Jakob N. Foerster;Gregory Farquhar;Triantafyllos Afouras;Nantas Nardelli.
national conference on artificial intelligence (2018)

660 Citations

Counterfactual Multi−Agent Policy Gradients

Jakob N. Foerster;Gregory Farquhar;Triantafyllos Afouras;Nantas Nardelli.
national conference on artificial intelligence (2018)

660 Citations

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid;Mikayel Samvelyan;Christian Schroeder;Gregory Farquhar.
international conference on machine learning (2018)

554 Citations

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Tabish Rashid;Mikayel Samvelyan;Christian Schroeder;Gregory Farquhar.
international conference on machine learning (2018)

554 Citations

A survey of multi-objective sequential decision-making

Diederik M. Roijers;Peter Vamplew;Shimon Whiteson;Richard Dazeley.
Journal of Artificial Intelligence Research (2013)

428 Citations

A survey of multi-objective sequential decision-making

Diederik M. Roijers;Peter Vamplew;Shimon Whiteson;Richard Dazeley.
Journal of Artificial Intelligence Research (2013)

428 Citations

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