Statements (27)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Reinforcement Learning Method
|
| gptkbp:advantage |
Captures risk and uncertainty
More robust learning |
| gptkbp:application |
gptkb:robot
gptkb:Atari_Games Finance |
| gptkbp:contrastsWith |
Expected Value RL
|
| gptkbp:enables |
Improved performance
Better exploration Uncertainty estimation |
| gptkbp:field |
gptkb:Machine_Learning
gptkb:artificial_intelligence gptkb:Reinforcement_Learning |
| gptkbp:focusesOn |
Learning the distribution of returns
|
| gptkbp:hasModel |
Random return distribution
|
| https://www.w3.org/2000/01/rdf-schema#label |
Distributional RL
|
| gptkbp:improves |
Value-based RL methods
|
| gptkbp:introduced |
gptkb:Marc_G._Bellemare
|
| gptkbp:introducedIn |
2017
|
| gptkbp:key |
A Distributional Perspective on Reinforcement Learning
|
| gptkbp:relatedTo |
C51 Algorithm
Implicit Quantile Networks Quantile Regression DQN |
| gptkbp:usedIn |
Deep RL Algorithms
|
| gptkbp:uses |
Probability distributions
|
| gptkbp:bfsParent |
gptkb:Rainbow_DQN
|
| gptkbp:bfsLayer |
7
|