Martin Riedmiller

E200561

Martin Riedmiller is a German computer scientist and pioneer in deep reinforcement learning, known for his influential work on neural-network-based control and contributions to landmark deep RL systems.

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All labels observed (1)

Label Occurrences
Martin Riedmiller canonical 2

Statements (47)

Predicate Object
instanceOf German person
computer scientist
person
researcher
affiliation DeepMind
countryOfAcademicInstitution Germany
countryOfResidence United Kingdom
employerType research company
fieldOfWork artificial intelligence
computer science
deep reinforcement learning
machine learning
reinforcement learning
hasAcademicAdvisor unknown
hasAcademicAffiliation University of Freiburg NERFINISHED
hasCitizenship Germany
hasContribution bridging reinforcement learning and control engineering
early practical algorithms for neural-network-based reinforcement learning
industrial applications of reinforcement learning
hasEmployer DeepMind
hasGender male
hasNotableStudent unknown
hasOccupation artificial intelligence researcher
computer scientist
machine learning researcher
hasRole research scientist
team lead
isPioneerIn deep reinforcement learning
neural-network-based control
knownFor applications of reinforcement learning to robotics
deep reinforcement learning
landmark deep reinforcement learning systems
neural-network-based control
Neural Architecture Search
surface form: neuroevolution of augmenting topologies for control tasks

work on value function approximation with neural networks
languageOfWorkOrName English
German
memberOf DeepMind
surface form: DeepMind research team
nationality German
notableWork neural fitted Q-iteration (NFQ)
work on off-policy reinforcement learning with neural networks
positionHeld professor of computer science at University of Freiburg
researchInterest autonomous agents
control
deep learning
robotics
sequential decision making

Referenced by (2)

Full triples — surface form annotated when it differs from this entity's canonical label.

Atari deep Q-network coAuthor Martin Riedmiller
Volodymyr Mnih coAuthorWith Martin Riedmiller