Ioannis Antonoglou
E203073
Ioannis Antonoglou is a computer scientist and DeepMind researcher known for his contributions to deep reinforcement learning, including work on the Atari deep Q-network.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Ioannis Antonoglou canonical | 4 |
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
DeepMind researcher
ⓘ
computer scientist ⓘ researcher ⓘ |
| affiliation |
DeepMind
ⓘ
surface form:
DeepMind Technologies
|
| coAuthorOf |
Atari deep Q-network
ⓘ
surface form:
Human-level control through deep reinforcement learning
MuZero ⓘ
surface form:
Mastering Atari, Go, Chess and Shogi by planning with a learned model
AlphaGo Zero ⓘ
surface form:
Mastering the game of Go with deep neural networks and tree search
AlphaGo Zero ⓘ
surface form:
Mastering the game of Go without human knowledge
Atari deep Q-network ⓘ
surface form:
Playing Atari with Deep Reinforcement Learning
|
| coAuthorWith |
Aja Huang
ⓘ
David Silver ⓘ Demis Hassabis ⓘ Julian Schrittwieser ⓘ Koray Kavukcuoglu ⓘ Shane Legg ⓘ Volodymyr Mnih ⓘ |
| employer |
DeepMind
ⓘ
surface form:
Google DeepMind
|
| fieldOfWork |
artificial intelligence
ⓘ
deep learning ⓘ machine learning ⓘ reinforcement learning ⓘ |
| gender | male ⓘ |
| hasExpertise |
game AI
ⓘ
neural networks ⓘ search algorithms ⓘ |
| knownFor |
AlphaGo
ⓘ
AlphaZero ⓘ Atari deep Q-network ⓘ Atari deep Q-network ⓘ
surface form:
DQN
MuZero ⓘ deep reinforcement learning ⓘ |
| language |
English
ⓘ
Greek ⓘ |
| nationality | Greek ⓘ |
| notableWork |
contributions to AlphaGo system
ⓘ
contributions to AlphaZero algorithm ⓘ contributions to Atari DQN ⓘ contributions to MuZero algorithm ⓘ |
| publicationVenue |
ICML
ⓘ
NeurIPS ⓘ
surface form:
NIPS/NeurIPS
Nature ⓘ arXiv ⓘ |
| researchArea |
Monte Carlo tree search
ⓘ
game-playing AI ⓘ model-based reinforcement learning ⓘ value-based reinforcement learning ⓘ |
| worksOn |
deep neural network architectures for control
ⓘ
large-scale reinforcement learning systems ⓘ |
Referenced by (4)
Full triples — surface form annotated when it differs from this entity's canonical label.