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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Martin Riedmiller canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T1793169 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martin Riedmiller Context triple: [Atari deep Q-network, coAuthor, Martin Riedmiller]
-
A.
Wolfram Burgard
Wolfram Burgard is a German computer scientist and roboticist known for his influential work in probabilistic robotics, autonomous navigation, and artificial intelligence.
-
B.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
-
C.
Harald Ganzinger
Harald Ganzinger was a prominent German computer scientist known for his influential work in automated theorem proving and term rewriting systems.
-
D.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
-
E.
Martin Benrath
Martin Benrath was a German actor known for his extensive work in film, television, and theater from the mid-20th century onward.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Martin Riedmiller Target entity description: 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.
-
A.
Wolfram Burgard
Wolfram Burgard is a German computer scientist and roboticist known for his influential work in probabilistic robotics, autonomous navigation, and artificial intelligence.
-
B.
Dario Amodei
Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
-
C.
Harald Ganzinger
Harald Ganzinger was a prominent German computer scientist known for his influential work in automated theorem proving and term rewriting systems.
-
D.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
-
E.
Martin Benrath
Martin Benrath was a German actor known for his extensive work in film, television, and theater from the mid-20th century onward.
- F. None of above. chosen
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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Martin Riedmiller Description of subject: 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.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.