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How to talk so AI will learn: Instructions, descriptions, and autonomy
v1v2v3 (latest)

How to talk so AI will learn: Instructions, descriptions, and autonomy

16 June 2022
T. Sumers
Robert D. Hawkins
Mark K. Ho
Thomas Griffiths
Dylan Hadfield-Menell
    LM&Ro
ArXiv (abs)PDFHTML

Papers citing "How to talk so AI will learn: Instructions, descriptions, and autonomy"

25 / 25 papers shown
Title
Problem Solving Through Human-AI Preference-Based Cooperation
Problem Solving Through Human-AI Preference-Based Cooperation
Subhabrata Dutta
Timo Kaufmann
Goran Glavaš
Ivan Habernal
Kristian Kersting
Frauke Kreuter
Mira Mezini
Iryna Gurevych
Eyke Hüllermeier
Hinrich Schuetze
205
2
0
14 Aug 2024
Leveraging Language for Accelerated Learning of Tool Manipulation
Leveraging Language for Accelerated Learning of Tool Manipulation
Allen Z. Ren
Bharat Govil
Tsung-Yen Yang
Karthik Narasimhan
Anirudha Majumdar
LM&Ro
79
36
0
27 Jun 2022
Correcting Robot Plans with Natural Language Feedback
Correcting Robot Plans with Natural Language Feedback
Pratyusha Sharma
Balakumar Sundaralingam
Valts Blukis
Chris Paxton
Tucker Hermans
Antonio Torralba
Jacob Andreas
Dieter Fox
3DVLM&Ro
67
93
0
11 Apr 2022
Semantic Exploration from Language Abstractions and Pretrained
  Representations
Semantic Exploration from Language Abstractions and Pretrained Representations
Allison C. Tam
Neil C. Rabinowitz
Andrew Kyle Lampinen
Nicholas A. Roy
Stephanie C. Y. Chan
D. Strouse
Jane X. Wang
Andrea Banino
Felix Hill
LM&Ro
104
70
0
08 Apr 2022
Inferring Rewards from Language in Context
Inferring Rewards from Language in Context
Jessy Lin
Daniel Fried
Dan Klein
Anca Dragan
LM&Ro
81
55
0
05 Apr 2022
On the Expressivity of Markov Reward
On the Expressivity of Markov Reward
David Abel
Will Dabney
Anna Harutyunyan
Mark K. Ho
Michael L. Littman
Doina Precup
Satinder Singh
82
85
0
01 Nov 2021
Skill Induction and Planning with Latent Language
Skill Induction and Planning with Latent Language
Pratyusha Sharma
Antonio Torralba
Jacob Andreas
LM&Ro
245
111
0
04 Oct 2021
Leveraging Language to Learn Program Abstractions and Search Heuristics
Leveraging Language to Learn Program Abstractions and Search Heuristics
Catherine Wong
Kevin Ellis
J. Tenenbaum
Jacob Andreas
82
56
0
18 Jun 2021
Extending rational models of communication from beliefs to actions
Extending rational models of communication from beliefs to actions
T. Sumers
Robert D. Hawkins
Mark K. Ho
Thomas Griffiths
73
16
0
25 May 2021
Interactive Learning from Activity Description
Interactive Learning from Activity Description
Khanh Nguyen
Dipendra Kumar Misra
Robert Schapire
Miroslav Dudík
Patrick Shafto
99
35
0
13 Feb 2021
Learning Rewards from Linguistic Feedback
Learning Rewards from Linguistic Feedback
T. Sumers
Mark K. Ho
Robert D. Hawkins
Karthik Narasimhan
Thomas Griffiths
125
54
0
30 Sep 2020
Reward-rational (implicit) choice: A unifying formalism for reward
  learning
Reward-rational (implicit) choice: A unifying formalism for reward learning
Hong Jun Jeon
S. Milli
Anca Dragan
76
177
0
12 Feb 2020
A mathematical theory of cooperative communication
A mathematical theory of cooperative communication
Pei Wang
Junqi Wang
P. Paranamana
Patrick Shafto
37
48
0
07 Oct 2019
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
Rohin Shah
Noah Gundotra
Pieter Abbeel
Anca Dragan
49
72
0
23 Jun 2019
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang
S. Gu
Kevin Patrick Murphy
Chelsea Finn
OffRL
57
225
0
18 Jun 2019
A Survey of Reinforcement Learning Informed by Natural Language
A Survey of Reinforcement Learning Informed by Natural Language
Jelena Luketina
Nantas Nardelli
Gregory Farquhar
Jakob N. Foerster
Jacob Andreas
Edward Grefenstette
Shimon Whiteson
Tim Rocktaschel
LM&RoKELMOffRLLRM
90
282
0
10 Jun 2019
When redundancy is useful: A Bayesian approach to óverinformative'
  referring expressions
When redundancy is useful: A Bayesian approach to óverinformative' referring expressions
Judith Degen
Robert D. Hawkins
Caroline Graf
Elisa Kreiss
Noah D. Goodman
63
81
0
19 Mar 2019
Using Natural Language for Reward Shaping in Reinforcement Learning
Using Natural Language for Reward Shaping in Reinforcement Learning
Prasoon Goyal
S. Niekum
Raymond J. Mooney
LM&Ro
92
183
0
05 Mar 2019
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep
  Networks for Thompson Sampling
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
C. Riquelme
George Tucker
Jasper Snoek
BDL
85
366
0
26 Feb 2018
Inverse Reward Design
Inverse Reward Design
Dylan Hadfield-Menell
S. Milli
Pieter Abbeel
Stuart J. Russell
Anca Dragan
86
399
0
08 Nov 2017
Pragmatic-Pedagogic Value Alignment
Pragmatic-Pedagogic Value Alignment
J. F. Fisac
Monica A. Gates
Jessica B. Hamrick
Chang-rui Liu
Dylan Hadfield-Menell
Malayandi Palaniappan
Dhruv Malik
S. Shankar Sastry
Thomas Griffiths
Anca Dragan
43
79
0
20 Jul 2017
Deep reinforcement learning from human preferences
Deep reinforcement learning from human preferences
Paul Christiano
Jan Leike
Tom B. Brown
Miljan Martic
Shane Legg
Dario Amodei
218
3,377
0
12 Jun 2017
Colors in Context: A Pragmatic Neural Model for Grounded Language
  Understanding
Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding
Will Monroe
Robert D. Hawkins
Noah D. Goodman
Christopher Potts
72
124
0
29 Mar 2017
Concrete Problems in AI Safety
Concrete Problems in AI Safety
Dario Amodei
C. Olah
Jacob Steinhardt
Paul Christiano
John Schulman
Dandelion Mané
248
2,405
0
21 Jun 2016
Reasoning About Pragmatics with Neural Listeners and Speakers
Reasoning About Pragmatics with Neural Listeners and Speakers
Jacob Andreas
Dan Klein
ReLMLRM
86
175
0
02 Apr 2016
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