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On the Feasibility of Learning, Rather than Assuming, Human Biases for
  Reward Inference

On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference

23 June 2019
Rohin Shah
Noah Gundotra
Pieter Abbeel
Anca Dragan
ArXivPDFHTML

Papers citing "On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference"

14 / 14 papers shown
Title
ARMCHAIR: integrated inverse reinforcement learning and model predictive
  control for human-robot collaboration
ARMCHAIR: integrated inverse reinforcement learning and model predictive control for human-robot collaboration
Angelo Caregnato-Neto
Luciano Cavalcante Siebert
Arkady Zgonnikov
Marcos Ricardo Omena de Albuquerque Máximo
R. J. Afonso
37
2
0
29 Feb 2024
Towards Understanding Sycophancy in Language Models
Towards Understanding Sycophancy in Language Models
Mrinank Sharma
Meg Tong
Tomasz Korbak
David Duvenaud
Amanda Askell
...
Oliver Rausch
Nicholas Schiefer
Da Yan
Miranda Zhang
Ethan Perez
216
198
0
20 Oct 2023
Discovering User Types: Mapping User Traits by Task-Specific Behaviors
  in Reinforcement Learning
Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning
L. L. Ankile
B. S. Ham
K. Mao
E. Shin
S. Swaroop
F. Doshi-Velez
W. Pan
OffRL
16
1
0
16 Jul 2023
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased
Chao Yu
Jiaxuan Gao
Weiling Liu
Bo Xu
Hao Tang
Jiaqi Yang
Yu Wang
Yi Wu
31
39
0
03 Feb 2023
On the Sensitivity of Reward Inference to Misspecified Human Models
On the Sensitivity of Reward Inference to Misspecified Human Models
Joey Hong
Kush S. Bhatia
Anca Dragan
19
24
0
09 Dec 2022
Misspecification in Inverse Reinforcement Learning
Misspecification in Inverse Reinforcement Learning
Joar Skalse
Alessandro Abate
33
22
0
06 Dec 2022
Modeling Mobile Health Users as Reinforcement Learning Agents
Modeling Mobile Health Users as Reinforcement Learning Agents
Eura Shin
S. Swaroop
Weiwei Pan
S. Murphy
Finale Doshi-Velez
OffRL
15
3
0
01 Dec 2022
Law Informs Code: A Legal Informatics Approach to Aligning Artificial
  Intelligence with Humans
Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans
John J. Nay
ELM
AILaw
88
27
0
14 Sep 2022
Humans are not Boltzmann Distributions: Challenges and Opportunities for
  Modelling Human Feedback and Interaction in Reinforcement Learning
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning
David Lindner
Mennatallah El-Assady
OffRL
30
16
0
27 Jun 2022
How to talk so AI will learn: Instructions, descriptions, and autonomy
How to talk so AI will learn: Instructions, descriptions, and autonomy
T. Sumers
Robert D. Hawkins
Mark K. Ho
Thomas L. Griffiths
Dylan Hadfield-Menell
LM&Ro
32
20
0
16 Jun 2022
Human irrationality: both bad and good for reward inference
Human irrationality: both bad and good for reward inference
Lawrence Chan
Andrew Critch
Anca Dragan
12
25
0
12 Nov 2021
Uncertain Decisions Facilitate Better Preference Learning
Uncertain Decisions Facilitate Better Preference Learning
Cassidy Laidlaw
Stuart J. Russell
30
11
0
19 Jun 2021
Deep Interpretable Models of Theory of Mind
Deep Interpretable Models of Theory of Mind
Ini Oguntola
Dana Hughes
Katia P. Sycara
HAI
33
23
0
07 Apr 2021
Conservative AI and social inequality: Conceptualizing alternatives to
  bias through social theory
Conservative AI and social inequality: Conceptualizing alternatives to bias through social theory
Mike Zajko
16
37
0
16 Jul 2020
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