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We study interactive learning in a setting where the agent has to generate a response (e.g., an action or trajectory) given a context and an instruction.
Adjustment of an inverse matrix corresponding to changes in the elements of a given column or a given row of the original matrix
Jack Sherman · 1949
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Understanding natural language
Terry Winograd · 1972
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On tail probabilities for martingales
David A Freedman · 1975
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Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau · 1991
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Empirical Processes in M-estimation , volume 6
Sara A Geer · 2000
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Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
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Logarithmic online regret bounds for undiscounted reinforcement learning
Peter Auer and Ronald Ortner · 2006
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Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
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Contextual bandit algorithms with supervised learning guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
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Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Thompson sampling for contextual bandits with linear payoffs
Shipra Agrawal and Navin Goyal · 2013
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Eluder dimension and the sample complexity of optimistic exploration
Daniel Russo and Benjamin Van Roy · 2013
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Taming the monster: A fast and simple algorithm for contextual bandits
Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert Schapire · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Listen, attend, and walk: Neural mapping of navigational instructions to action sequences
Hongyuan Mei, Mohit Bansal, and Matthew Walter · 2016
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Tell me dave: Context-sensitive grounding of natural language to manipulation instructions
Dipendra K Misra, Jaeyong Sung, Kevin Lee, and Ashutosh Saxena · 2016
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Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
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Speaker-follower models for vision-and-language navigation
Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, and Trevor Darrell · 2018
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Kinematic state abstraction and provably efficient rich-observation reinforcement learning
Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, and John Langford · 2020
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The statistical complexity of interactive decision making
Dylan J Foster, Sham M Kakade, Jian Qian, and Alexander Rakhlin · 2021
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Grounded language learning fast and slow
Felix Hill, Olivier Tieleman, Tamara von Glehn, Nathaniel Wong, Hamza Merzic, and Stephen Clark · 2021
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Bellman eluder dimension: New rich classes of rl problems, and sample-efficient algorithms
Chi Jin, Qinghua Liu, and Sobhan Miryoosefi · 2021
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Interactive learning from activity description
Khanh X Nguyen, Dipendra Misra, Robert Schapire, Miroslav Dudík, and Patrick Shafto · 2021
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Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, and Yoav Artzi · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Learning to map natural language instructions to physical quadcopter control using simulated flight
Valts Blukis, Yannick Terme, Eyvind Niklasson, Ross A. Knepper, and Yoav Artzi · 2019
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High-dimensional statistics: A non-asymptotic viewpoint , volume 48
Martin J Wainwright · 2019
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Flambe: Structural complexity and representation learning of low rank mdps
Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, and Wen Sun · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Stochastic bandits with linear constraints
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, and Heinrich Jiang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Bridging offline reinforcement learning and imitation learning: A tale of pessimism
Paria Rashidinejad, Banghua Zhu, Cong Ma, Jiantao Jiao, and Stuart Russell · 2021
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Agnostic reinforcement learning with low-rank mdps and rich observations
Ayush Sekhari, Christoph Dann, Mehryar Mohri, Yishay Mansour, and Karthik Sridharan · 2021
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Exploration via elliptical episodic bonuses
Mikael Henaff, Roberta Raileanu, Minqi Jiang, and Tim Rocktäschel · 2022
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Making linear mdps practical via contrastive representation learning
Tianjun Zhang, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, and Bo Dai · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Tight guarantees for interactive decision making with the decision-estimation coefficient
Dylan J Foster, Noah Golowich, and Yanjun Han · 2023
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Goal representations for instruction following: A semi-supervised language interface to control
Vivek Myers, Andre Wang He, Kuan Fang, Homer Rich Walke, Philippe Hansen-Estruch, Ching-An Cheng, Mihai Jalobeanu, Andrey Kolobov, Anca Dragan, and Sergey Levine · 2023
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