Statements (22)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
inverse reinforcement learning algorithm
|
| gptkbp:alsoKnownAs |
MaxEnt IRL
|
| gptkbp:assumes |
demonstrations are noisy or stochastic
|
| gptkbp:basedOn |
gptkb:maximum_entropy_principle
|
| gptkbp:citation |
Ziebart, B. D., Maas, A. L., Bagnell, J. A., & Dey, A. K. (2008). Maximum entropy inverse reinforcement learning. AAAI.
|
| gptkbp:extendsTo |
classic IRL
|
| gptkbp:form |
probability of trajectory proportional to exponential of reward
|
| gptkbp:goal |
recover reward function from expert demonstrations
|
| https://www.w3.org/2000/01/rdf-schema#label |
maximum entropy IRL
|
| gptkbp:influenced |
deep maximum entropy IRL
generative adversarial imitation learning |
| gptkbp:introduced |
Brian Ziebart
|
| gptkbp:introducedIn |
2008
|
| gptkbp:output |
stochastic policy
|
| gptkbp:relatedTo |
gptkb:reinforcement_learning
inverse reinforcement learning apprenticeship learning |
| gptkbp:usedIn |
robotics
autonomous driving imitation learning |
| gptkbp:bfsParent |
gptkb:Inverse_Reinforcement_Learning
|
| gptkbp:bfsLayer |
8
|