Fetching the paper…
Reading the bibliography…
Imitation learning holds tremendous promise in learning policies efficiently for complex decision making problems.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 1910
Earlier work this paper cites.
A markovian decision process
R. Bellman · 1957
Earlier work this paper cites.
Concerning nonnegative matrices and doubly stochastic matrices
R. Sinkhorn and P. Knopp · 1967
Earlier work this paper cites.
An autonomous land vehicle in a neural network
D. Pomerleau · 1998
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
A. Y. Ng, S. J. Russell, et al · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
P. Abbeel and A. Y. Ng · 2004
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, A. K. Dey, et al · 2008
Earlier work this paper cites.
The sinkhorn–knopp algorithm: convergence and applications
P. A. Knight · 2008
Earlier work this paper cites.
Optimal transport: old and new , volume 338
C. Villani · 2009
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
Earlier work this paper cites.
Deterministic policy gradient algorithms
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
Earlier work this paper cites.
Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
Earlier work this paper cites.
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
Earlier work this paper cites.
Gromov-wasserstein averaging of kernel and distance matrices
G. Peyré, M. Cuturi, and J. Solomon · 2016
Earlier work this paper cites.
Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Earlier work this paper cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A. Rajeswaran, V. Kumar, A. Gupta, G. Vezzani, J. Schulman, E. Todorov, and S. Levine · 2017
Earlier work this paper cites.
Hindsight experience replay
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba · 2017
Cited alongside, same era.
Learning robust rewards with adversarial inverse reinforcement learning
J. Fu, K. Luo, and S. Levine · 2017
Cited alongside, same era.
Imitation learning: A survey of learning methods
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne · 2017
Cited alongside, same era.
Soft-dtw: a differentiable loss function for time-series
M. Cuturi and M. Blondel · 2017
Cited alongside, same era.
I. Kostrikov, K. K. Agrawal, D. Dwibedi, S. Levine, and J. Tompson · 2018
Cited alongside, same era.
Augmenting gail with bc for sample efficient imitation learning
R. Jena, C. Liu, and K. Sycara · 2020
Later among the works it cites.
Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine · 2020
Later among the works it cites.
A divergence minimization perspective on imitation learning methods
S. K. S. Ghasemipour, R. Zemel, and S. Gu · 2020
Later among the works it cites.
The magical benchmark for robust imitation
S. Toyer, R. Shah, A. Critch, and S. Russell · 2020
Later among the works it cites.
I. Redko, T. Vayer, R. Flamary, and N. Courty · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Tassa, Y. Doron, A. Muldal, T. Erez, Y. Li, D. d. L. Casas, D. Budden, A. Abdolmaleki, J. Merel, A. Lefrancq, et al · 2018
Cited alongside, same era.
Overcoming exploration in reinforcement learning with demonstrations
A. Nair, B. McGrew, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
Cited alongside, same era.
Multi-goal reinforcement learning: Challenging robotics environments and request for research
M. Plappert, M. Andrychowicz, A. Ray, B. McGrew, B. Baker, G. Powell, J. Schneider, J. Tobin, M. Chociej, P. Welinder, et al · 2018
Cited alongside, same era.
Generative adversarial imitation from observation
F. Torabi, G. Warnell, and P. Stone · 2018
Cited alongside, same era.
Reinforcement learning: An introduction
R. S. Sutton and A. G. Barto · 2018
Cited alongside, same era.
Recent advances in imitation learning from observation
F. Torabi, G. Warnell, and P. Stone · 2019
Cited alongside, same era.
Computational optimal transport: With applications to data science
G. Peyré, M. Cuturi, et al · 2019
Cited alongside, same era.
S. Levine, A. Kumar, G. Tucker, and J. Fu · 2020
Later among the works it cites.
Keep doing what worked: Behavioral modelling priors for offline reinforcement learning
N. Y. Siegel, J. T. Springenberg, F. Berkenkamp, A. Abdolmaleki, M. Neunert, T. Lampe, R. Hafner, N. Heess, and M. Riedmiller · 2020
Later among the works it cites.
State-only imitation learning for dexterous manipulation
I. Radosavovic, X. Wang, L. Pinto, and J. Malik · 2020
Later among the works it cites.
A framework for efficient robotic manipulation
A. Zhan, P. Zhao, L. Pinto, P. Abbeel, and M. Laskin · 2020
Later among the works it cites.
S. Young, D. Gandhi, S. Tulsiani, A. Gupta, P. Abbeel, and L. Pinto · 2020
Later among the works it cites.
Mastering visual continuous control: Improved data-augmented reinforcement learning
D. Yarats, R. Fergus, A. Lazaric, and L. Pinto · 2021
Later among the works it cites.
The surprising effectiveness of representation learning for visual imitation
J. Pari, N. Muhammad, S. P. Arunachalam, and L. Pinto · 2021
Later among the works it cites.
Domain-robust visual imitation learning with mutual information constraints
E. Cetin and O. Celiktutan · 2021
Later among the works it cites.
Visual adversarial imitation learning using variational models
R. Rafailov, T. Yu, A. Rajeswaran, and C. Finn · 2021
Later among the works it cites.
Aligning time series on incomparable spaces
S. Cohen, G. Luise, A. Terenin, B. Amos, and M. Deisenroth · 2021
Later among the works it cites.
Cross-domain imitation learning via optimal transport
A. Fickinger, S. Cohen, S. Russell, and B. Amos · 2021
Later among the works it cites.
A minimalist approach to offline reinforcement learning
S. Fujimoto and S. S. Gu · 2021
Later among the works it cites.
{OPAL}: Offline primitive discovery for accelerating offline reinforcement learning
A. Ajay, A. Kumar, P. Agrawal, S. Levine, and O. Nachum · 2021
Later among the works it cites.
Jump-start reinforcement learning
I. Uchendu, T. Xiao, Y. Lu, B. Zhu, M. Yan, J. Simon, M. Bennice, C. Fu, C. Ma, J. Jiao, et al · 2022
Closest in time.
Imitation learning from pixel observations for continuous control, 2022
S. Cohen, B. Amos, M. P. Deisenroth, M. Henaff, E. Vinitsky, and D. Yarats · 2022
Closest in time.
Dexterous imitation made easy: A learning-based framework for efficient dexterous manipulation
S. P. Arunachalam, S. Silwal, B. Evans, and L. Pinto · 2022
Closest in time.