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Identifiability and generalizability from multiple experts in Inverse
  Reinforcement Learning

Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning

22 September 2022
Paul Rolland
Luca Viano
Norman Schuerhoff
Boris Nikolov
V. Cevher
    OffRL
ArXivPDFHTML

Papers citing "Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning"

5 / 5 papers shown
Title
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
Andreas Schlaginhaufen
Maryam Kamgarpour
OffRL
23
1
0
03 Jun 2024
Identifiability and Generalizability in Constrained Inverse
  Reinforcement Learning
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
Andreas Schlaginhaufen
Maryam Kamgarpour
29
10
0
01 Jun 2023
When Demonstrations Meet Generative World Models: A Maximum Likelihood
  Framework for Offline Inverse Reinforcement Learning
When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
Siliang Zeng
Chenliang Li
Alfredo García
Min-Fong Hong
OffRL
34
13
0
15 Feb 2023
Environment Design for Inverse Reinforcement Learning
Environment Design for Inverse Reinforcement Learning
Thomas Kleine Buening
Victor Villin
Christos Dimitrakakis
32
1
0
26 Oct 2022
Reward (Mis)design for Autonomous Driving
Reward (Mis)design for Autonomous Driving
W. B. Knox
A. Allievi
Holger Banzhaf
Felix Schmitt
Peter Stone
83
113
0
28 Apr 2021
1