Dan Horgan
E736832
deep reinforcement learning algorithm
machine learning researcher
person
reinforcement learning algorithm
scientific paper
Dan Horgan is a machine learning researcher known for co-authoring the influential Rainbow DQN algorithm that combines multiple deep reinforcement learning improvements into a single unified agent.
Observed surface forms (2)
| Surface form | Occurrences |
|---|---|
| Rainbow: Combining Improvements in Deep Reinforcement Learning | 0 |
| Rainbow DQN | 0 |
Statements (39)
| Predicate | Object |
|---|---|
| instanceOf |
deep reinforcement learning algorithm
ⓘ
machine learning researcher ⓘ person ⓘ reinforcement learning algorithm ⓘ scientific paper ⓘ |
| affiliation | DeepMind NERFINISHED ⓘ |
| appliesTo | Atari 2600 games ⓘ |
| author |
Dan Horgan
NERFINISHED
ⓘ
David Silver NERFINISHED ⓘ Georg Ostrovski NERFINISHED ⓘ Hado van Hasselt NERFINISHED ⓘ Joseph Modayil NERFINISHED ⓘ Matteo Hessel NERFINISHED ⓘ Tom Schaul NERFINISHED ⓘ Will Dabney NERFINISHED ⓘ |
| basedOn | Deep Q-Network NERFINISHED ⓘ |
| coAuthorOf | Rainbow: Combining Improvements in Deep Reinforcement Learning NERFINISHED ⓘ |
| combinesMethod |
Distributional RL
ⓘ
Double DQN NERFINISHED ⓘ Dueling Network Architectures NERFINISHED ⓘ Multi-step Learning ⓘ Noisy Nets NERFINISHED ⓘ Prioritized Experience Replay NERFINISHED ⓘ |
| developer |
Dan Horgan
NERFINISHED
ⓘ
DeepMind NERFINISHED ⓘ Hado van Hasselt NERFINISHED ⓘ Matteo Hessel NERFINISHED ⓘ |
| fieldOfWork |
artificial intelligence
ⓘ
deep reinforcement learning ⓘ machine learning ⓘ reinforcement learning ⓘ |
| introducedAlgorithm | Rainbow DQN NERFINISHED ⓘ |
| mainSubject |
deep reinforcement learning
ⓘ
value-based reinforcement learning ⓘ |
| notableFor | co-developing the Rainbow DQN algorithm ⓘ |
| notableWork | Rainbow: Combining Improvements in Deep Reinforcement Learning NERFINISHED ⓘ |
| publicationYear | 2017 ⓘ |
| worksOn |
neural network function approximation in RL
ⓘ
value-based deep reinforcement learning ⓘ |
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.