Fetching the paper…
Reading the bibliography…
The performance of imitation learning is typically upper-bounded by the performance of the demonstrator.
Introduction to reinforcement learning , volume 135
R. S. Sutton and A. G. Barto · 1998
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
A. Y. Ng, D. Harada, and S. Russell · 1999
Earlier work this paper cites.
A framework for behavioural claning
M. Bain and C. Sommut · 1999
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
A. Y. Ng and S. J. Russell · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
Earlier work this paper cites.
Learning to rank: from pairwise approach to listwise approach
Z. Cao, T. Qin, T.-Y. Liu, M.-F. Tsai, and H. Li · 2007
Earlier work this paper cites.
A game-theoretic approach to apprenticeship learning
U. Syed and R. E. Schapire · 2008
Earlier work this paper cites.
Active preference learning with discrete choice data
B. Eric, N. D. Freitas, and A. Ghosh · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
Earlier work this paper cites.
Ranking measures and loss functions in learning to rank
W. Chen, T.-Y. Liu, Y. Lan, Z.-M. Ma, and H. Li · 2009
Earlier work this paper cites.
Efficient reductions for imitation learning
S. Ross and D. Bagnell · 2010
Earlier work this paper cites.
Integrating reinforcement learning with human demonstrations of varying ability
M. E. Taylor, H. B. Suay, and S. Chernova · 2011
Earlier work this paper cites.
Skill learning and task outcome prediction for manipulation
P. Pastor, M. Kalakrishnan, S. Chitta, E. Theodorou, and S. Schaal · 2011
Earlier work this paper cites.
Donut as i do: Learning from failed demonstrations
D. H. Grollman and A. Billard · 2011
Earlier work this paper cites.
Relative entropy inverse reinforcement learning
A. Boularias, J. Kober, and J. Peters · 2011
Earlier work this paper cites.
Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective
B. Akgun, M. Cakmak, J. W. Yoo, and A. L. Thomaz · 2012
Earlier work this paper cites.
Individual choice behavior: A theoretical analysis
R. D. Luce · 2012
Earlier work this paper cites.
Learning objective functions for manipulation
M. Kalakrishnan, P. Pastor, L. Righetti, and S. Schaal · 2013
Earlier work this paper cites.
Robust bayesian inverse reinforcement learning with sparse behavior noise
J. Zheng, S. Liu, and L. M. Ni · 2014
Cited alongside, same era.
Guided cost learning: Deep inverse optimal control via policy optimization
C. Finn, S. Levine, and P. Abbeel · 2016
Cited alongside, same era.
Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
Cited alongside, same era.
Inverse reinforcement learning from failure
K. Shiarlis, J. Messias, and S. Whiteson · 2016
Cited alongside, same era.
Towards resolving unidentifiability in inverse reinforcement learning
K. Amin and S. Singh · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Reward learning from human preferences and demonstrations in atari
B. Ibarz, J. Leike, T. Pohlen, G. Irving, S. Legg, and D. Amodei · 2018
Later among the works it cites.
An algorithmic perspective on imitation learning
T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, J. Peters, et al · 2018
Later among the works it cites.
A survey of inverse reinforcement learning: Challenges, methods and progress
S. Arora and P. Doshi · 2018
Later among the works it cites.
Roboturk: A crowdsourcing platform for robotic skill learning through imitation
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, S. Savarese, and L. Fei-Fei · 2018
Later among the works it cites.
End-to-end driving via conditional imitation learning
F. Codevilla, M. Miiller, A. López, V. Koltun, and A. Dosovitskiy · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Ho and S. Ermon · 2016
Cited alongside, same era.
Deep tamer: Interactive agent shaping in high-dimensional state spaces
G. Warnell, N. Waytowich, V. Lawhern, and P. Stone · 2017
Cited alongside, same era.
Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
Cited alongside, same era.
Active preference-based learning of reward functions
D. Sadigh, A. D. Dragan, S. S. Sastry, and S. A. Seshia · 2017
Cited alongside, same era.
Dart: Noise injection for robust imitation learning
M. Laskey, J. Lee, R. Fox, A. Dragan, and K. Goldberg · 2017
Cited alongside, same era.
Statistical data cleaning for deep learning of automation tasks from demonstrations
C. Chuck, M. Laskey, S. Krishnan, R. Joshi, R. Fox, and K. Goldberg · 2017
Cited alongside, same era.
A comparison of remote robot teleoperation interfaces for general object manipulation
D. Kent, C. Saldanha, and S. Chernova · 2017
Cited alongside, same era.
Occam’s razor is insufficient to infer the preferences of irrational agents
S. Armstrong and S. Mindermann · 2018
Later among the works it cites.
Visualizing and understanding atari agents
S. Greydanus, A. Koul, J. Dodge, and A. Fern · 2018
Later among the works it cites.
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
D. S. Brown and S. Niekum · 2018
Later among the works it cites.
Risk-aware active inverse reinforcement learning
D. S. Brown, Y. Cui, and S. Niekum · 2018
Later among the works it cites.
Behavioral cloning from observation
F. Torabi, G. Warnell, and P. Stone · 2018
Later among the works it cites.
Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
D. Brown, W. Goo, N. Prabhat, and S. Niekum · 2019
Closest in time.
Learning reward functions by integrating human demonstrations and preferences
M. Palan, N. C. Landolfi, G. Shevchuk, and D. Sadigh · 2019
Closest in time.
A review of robot learning for manipulation: Challenges, representations, and algorithms
O. Kroemer, S. Niekum, and G. Konidaris · 2019
Closest in time.
Robust learning from demonstrations with mixed qualities using leveraged gaussian processes
S. Choi, K. Lee, and S. Oh · 2019
Closest in time.
Imitation learning from imperfect demonstration
Y.-H. Wu, N. Charoenphakdee, H. Bao, V. Tangkaratt, and M. Sugiyama · 2019
Closest in time.
Inverse reinforcement learning with multiple ranked experts
P. S. Castro, S. Li, and D. Zhang · 2019
Closest in time.
Machine teaching for inverse reinforcement learning: Algorithms and applications
D. S. Brown and S. Niekum · 2019
Closest in time.