Fetching the paper…
Reading the bibliography…
This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (without access to actions taken by the expert).
Alvinn: An autonomous land vehicle in a neural network
D. A. Pomerleau · 1989
Earlier work this paper cites.
First results with dyna, an integrated architecture for learning, planning, and reacting
R. S. Sutton · 1990
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart Russell · 2000
Earlier work this paper cites.
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
Ronen I. Brafman and Moshe Tennenholtz · 2001
Earlier work this paper cites.
A natural policy gradient
Sham M. Kakade · 2001
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer · 2002
Earlier work this paper cites.
Near-optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
Earlier work this paper cites.
Near optimal reinforcement learning in polynomial time
Michael Kearns and Satinder Singh · 2002
Earlier work this paper cites.
Exploration in metric state spaces
Sham M. Kakade, Michael J. Kearns, and John Langford · 2003
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y. Ng · 2004
Earlier work this paper cites.
Iterative linear quadratic regulator design for nonlinear biological movement systems
Weiwei Li and Emanuel Todorov · 2004
Earlier work this paper cites.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
Earlier work this paper cites.
Gaussian process optimization in the bandit setting: No regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, and Matthias Seeger · 2009
Earlier work this paper cites.
Near-optimal regret bounds for reinforcement learning
Thomas Jaksch, Ronald Ortner, and Peter Auer · 2010
Earlier work this paper cites.
Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
Earlier work this paper cites.
PILCO: A model-based and data-efficient approach to policy search
Marc Deisenroth and Carl E. Rasmussen · 2011
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J. Gordon, and Drew Bagnell · 2011
Earlier work this paper cites.
Regret analysis of stochastic and non-stochastic multi-armed bandit problems
Sébastien Bubeck and Nicolò Cesa-Bianchi · 2012
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Eluder dimension and the sample complexity of optimistic exploration
Daniel Russo and Benjamin Van Roy · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George E. Dahl, and Geoffrey E. Hinton · 2013
Earlier work this paper cites.
Approximate model-assisted neural fitted q-iteration
Thomas Lampe and Martin A. Riedmiller · 2014
Earlier work this paper cites.
Model-based reinforcement learning and the Eluder dimension
Ian Osband and Benjamin Van Roy · 2014
Cited alongside, same era.
Learning to optimize via posterior sampling
Daniel Russo and Benjamin Van Roy · 2014
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
Reinforcement learning: Theory and algorithms
Alekh Agarwal, Nan Jiang, Sham M Kakade, and Wen Sun · 2019
Later among the works it cites.
Exploration by random network distillation
Yuri Burda, Harrison Edwards, Amos J. Storkey, and Oleg Klimov · 2019
Later among the works it cites.
Imitating latent policies from observation
Ashley D. Edwards, Himanshu Sahni, Yannick Schroecker, and Charles L. Isbell Jr · 2019
Later among the works it cites.
When to trust your model: Model-based policy optimization
Michael Janner, Justin Fu, Marvin Zhang, and Sergey Levine · 2019
Later among the works it cites.
Imitation learning as f f -divergence minimization
Liyiming Ke, Matt Barnes, Wen Sun, Gilwoo Lee, Sanjiban Choudhury, and Siddhartha Srinivasa · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Cited alongside, same era.
Minimax regret bounds for reinforcement learning
Mohammad Gheshlaghi Azar, Ian Osband, and Rémi Munos · 2017
Cited alongside, same era.
Openai baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov · 2017
Cited alongside, same era.
Towards Generalization and Simplicity in Continuous Control
Aravind Rajeswaran, Kendall Lowrey, Emanuel Todorov, and Sham Kakade · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Playing hard exploration games by watching youtube
Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, and Nando de Freitas · 2018
Cited alongside, same era.
Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control
Kendall Lowrey, Aravind Rajeswaran, Sham Kakade, Emanuel Todorov, and Igor Mordatch · 2019
Later among the works it cites.
Self-supervised exploration via disagreement
Deepak Pathak, Dhiraj Gandhi, and Abhinav Gupta · 2019
Later among the works it cites.
Model-based rl in contextual decision processes: Pac bounds and exponential improvements over model-free approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, and John Langford · 2019
Later among the works it cites.
Provably efficient imitation learning from observation alone
Wen Sun, Anirudh Vemula, Byron Boots, and Drew Bagnell · 2019
Later among the works it cites.
Benchmarking model-based reinforcement learning
Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, and Jimmy Ba · 2019
Later among the works it cites.
Imitation learning from observations by minimizing inverse dynamics disagreement
Chao Yang, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu, Junzhou Huang, and Chuang Gan · 2019
Later among the works it cites.
Reinforcement leaning in feature space: Matrix bandit, kernels, and regret bound
Lin F Yang and Mengdi Wang · 2019
Later among the works it cites.
Efficient model-based reinforcement learning through optimistic policy search and planning
Sebastian Curi, Felix Berkenkamp, and Andreas Krause · 2020
Later among the works it cites.
An imitation from observation approach to transfer learning with dynamics mismatch
Siddharth Desai, Ishan Durugkar, Haresh Karnan, Garrett Warnell, Josiah Hanna, Peter Stone, and AI Sony · 2020
Later among the works it cites.
A divergence minimization perspective on imitation learning methods
Seyed Kamyar Seyed Ghasemipour, Richard Zemel, and Shixiang Gu · 2020
Later among the works it cites.
Information theoretic regret bounds for online nonlinear control
Sham Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, and Wen Sun · 2020
Later among the works it cites.
Information theoretic regret bounds for online nonlinear control
Sham M. Kakade, Akshay Krishnamurthy, Kendall Lowrey, Motoya Ohnishi, and Wen Sun · 2020
Later among the works it cites.
Morel: Model-based offline reinforcement learning
Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, and Thorsten Joachims · 2020
Later among the works it cites.
Bandit Algorithms
Tor Lattimore and Csaba Szepesvári · 2020
Later among the works it cites.
A game theoretic framework for model based reinforcement learning
Aravind Rajeswaran, Igor Mordatch, and Vikash Kumar · 2020
Later among the works it cites.
Reinforcement learning with videos: Combining offline observations with interaction
Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, and Chelsea Finn · 2020
Later among the works it cites.
Provably efficient model-based policy adaptation
Yuda Song, Aditi Mavalankar, Wen Sun, and Sicun Gao · 2020
Later among the works it cites.
Off-policy imitation learning from observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai, and Jiayu Zhou · 2020
Later among the works it cites.