Yuval Tassa
E441110
Yuval Tassa is a researcher in reinforcement learning and control who co-authored the work that introduced the Deep Deterministic Policy Gradient (DDPG) algorithm.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Yuval Tassa canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T4470501 — 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: Yuval Tassa Context triple: [DDPG, introducedBy, Yuval Tassa]
-
A.
Yuval Ishai
Yuval Ishai is a computer scientist known for his influential work in cryptography, particularly in secure multiparty computation and related areas of theoretical cryptography.
-
B.
Amir Shinar
Amir Shinar is an Israeli entrepreneur and software engineer best known as one of the co-founders of the GPS navigation and traffic app Waze.
-
C.
Yair Shamir
Yair Shamir is an Israeli businessman, former military officer, and politician who served as a government minister and is the son of former Prime Minister Yitzhak Shamir.
-
D.
Jeremy Shamos
Jeremy Shamos is an American stage and screen actor known for his work on Broadway and in film and television.
-
E.
Jonathan Shapiro
Jonathan Shapiro is a television writer and producer best known for co-creating the legal drama series "Goliath."
- 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: Yuval Tassa Target entity description: Yuval Tassa is a researcher in reinforcement learning and control who co-authored the work that introduced the Deep Deterministic Policy Gradient (DDPG) algorithm.
-
A.
Yuval Ishai
Yuval Ishai is a computer scientist known for his influential work in cryptography, particularly in secure multiparty computation and related areas of theoretical cryptography.
-
B.
Amir Shinar
Amir Shinar is an Israeli entrepreneur and software engineer best known as one of the co-founders of the GPS navigation and traffic app Waze.
-
C.
Yair Shamir
Yair Shamir is an Israeli businessman, former military officer, and politician who served as a government minister and is the son of former Prime Minister Yitzhak Shamir.
-
D.
Jeremy Shamos
Jeremy Shamos is an American stage and screen actor known for his work on Broadway and in film and television.
-
E.
Jonathan Shapiro
Jonathan Shapiro is a television writer and producer best known for co-creating the legal drama series "Goliath."
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
computer scientist
ⓘ
researcher ⓘ |
| coAuthorOf |
Continuous control with deep reinforcement learning
ⓘ
linked to:
Soft Actor-Critic
|
| coAuthorWith |
Alexander Pritzel
ⓘ
Andrej A. Rusu ⓘ Daan Wierstra ⓘ David Silver ⓘ Demis Hassabis ⓘ Guillaume Desjardins ⓘ Jonathan J. Hunt ⓘ Koray Kavukcuoglu ⓘ Martin Riedmiller ⓘ Nando de Freitas ⓘ Nicolas Heess ⓘ Raia Hadsell ⓘ Sergey Levine ⓘ Shakir Mohamed ⓘ Timothy P. Lillicrap ⓘ Tom Erez ⓘ Tom Schaul ⓘ |
| contributedTo |
applications of deep RL to continuous control tasks
ⓘ
development of DDPG ⓘ |
| fieldOfWork |
control theory
ⓘ
reinforcement learning ⓘ robotics ⓘ |
| hasPublicationType |
conference papers
ⓘ
journal articles ⓘ preprints ⓘ |
| hasResearchInterest |
continuous control
ⓘ
control in high-dimensional systems ⓘ deep reinforcement learning ⓘ deterministic policy gradients ⓘ model predictive control ⓘ model-based control ⓘ motor control ⓘ optimal control ⓘ policy gradient methods ⓘ robot control ⓘ simulation for control ⓘ trajectory optimization ⓘ |
| knownFor |
DDPG algorithm
ⓘ
linked to:
DDPG
Deep Deterministic Policy Gradient ⓘ
linked to:
DDPG
|
| worksOn |
continuous action spaces
ⓘ
deep learning for control ⓘ neural network policies ⓘ simulation-based reinforcement learning ⓘ |
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: Yuval Tassa Description of subject: Yuval Tassa is a researcher in reinforcement learning and control who co-authored the work that introduced the Deep Deterministic Policy Gradient (DDPG) algorithm.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.