Fetching the paper…
Reading the bibliography…
This paper presents a general framework for exploiting the representational capacity of neural networks to approximate complex, nonlinear reward functions in the context of solving the inverse reinforcement learning (IRL) problem.
Multilayer feedforward networks are universal approximators
Hornik, Kurt, Stinchcombe, Maxwell, and White, Halbert · 1989
Earlier work this paper cites.
Fundamentals of artificial neural networks
Hassoun, Mohamad H · 1995
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Abbeel, Pieter and Ng, Andrew Y · 2004
Earlier work this paper cites.
Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms , volume 97
Snyman, Jan · 2005
Earlier work this paper cites.
Maximum margin planning
Ratliff, Nathan D, Bagnell, J Andrew, and Zinkevich, Martin A · 2006
Earlier work this paper cites.
Scaling learning algorithms towards AI
Bengio, Yoshua, LeCun, Yann, et al · 2007
Earlier work this paper cites.
Bayesian inverse reinforcement learning
Ramachandran, Deepak and Amir, Eyal · 2007
Earlier work this paper cites.
A game-theoretic approach to apprenticeship learning
Syed, Umar and Schapire, Robert E · 2007
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Ziebart, Brian D, Maas, Andrew L, Bagnell, J Andrew, and Dey, Anind K · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
Argall, Brenna D, Chernova, Sonia, Veloso, Manuela, and Browning, Brett · 2009
Earlier work this paper cites.
Action understanding as inverse planning
Baker, Chris L, Saxe, Rebecca, and Tenenbaum, Joshua B · 2009
Cited alongside, same era.
Learning deep architectures for ai
Bengio, Yoshua · 2009
Cited alongside, same era.
Active learning for reward estimation in inverse reinforcement learning
Lopes, Manuel, Melo, Francisco, and Montesano, Luis · 2009
Cited alongside, same era.
Feature construction for inverse reinforcement learning
Levine, Sergey, Popovic, Zoran, and Koltun, Vladlen · 2010
Cited alongside, same era.
Apprenticeship learning about multiple intentions
Babes, Monica, Marivate, Vukosi, Subramanian, Kaushik, and Littman, Michael L · 2011
Cited alongside, same era.
Adaptive subgradient methods for online learning and stochastic optimization
Duchi, John, Hazan, Elad, and Singer, Yoram · 2011
Cited alongside, same era.
Apprenticeship learning using inverse reinforcement learning and gradient methods
Neu, Gergely and Szepesvári, Csaba · 2012
Later among the works it cites.
An application of inverse reinforcement learning to medical records of diabetes treatment
Asoh, Hideki, Akaho, Masanori Shiro1 Shotaro, Kamishima, Toshihiro, Hasida, Koiti, Aramaki, Eiji, and Kohro, Takahide · 2013
Later among the works it cites.
Bayesian nonparametric feature construction for inverse reinforcement learning
Choi, Jaedeug and Kim, Kee-Eung · 2013
Later among the works it cites.
Playing atari with deep reinforcement learning
Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Graves, Alex, Antonoglou, Ioannis, Wierstra, Daan, and Riedmiller, Martin · 2013
Later among the works it cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nonlinear inverse reinforcement learning with gaussian processes
Levine, Sergey, Popovic, Zoran, and Koltun, Vladlen · 2011
Cited alongside, same era.
Unsupervised feature learning and deep learning: A review and new perspectives
Bengio, Yoshua, Courville, Aaron C., and Vincent, Pascal · 2012
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E., Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2012
Cited alongside, same era.
Activity forecasting
Kitani, Kris, Ziebart, Brian, Bagnell, James, and Hebert, Martial · 2012
Cited alongside, same era.
Sermanet, Pierre, Eigen, David, Zhang, Xiang, Mathieu, Michaël, Fergus, Rob, and LeCun, Yann · 2013
Later among the works it cites.
Fully convolutional networks for semantic segmentation
Long, Jonathan, Shelhamer, Evan, and Darrell, Trevor · 2014
Later among the works it cites.
Joint training of a convolutional network and a graphical model for human pose estimation
Tompson, Jonathan, Jain, Arjun, LeCun, Yann, and Bregler, Christoph · 2014
Later among the works it cites.
Matconvnet – convolutional neural networks for matlab
Vedaldi, A. and Lenc, K · 2014
Later among the works it cites.
Learning deep vision-based costs and policies
Levine, Sergey, Finn, Chelsea, Darrell, Trevor, and Abbeel, Pieter · 2015
Closest in time.
Learning depth from single monocular images using deep convolutional neural fields
Liu, Fayao, Shen, Chunhua, Lin, Guosheng, and Reid, Ian D · 2015
Closest in time.