Fetching the paper…
Reading the bibliography…
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior.
Karel the robot: a gentle introduction to the art of programming
Pattis, R. E · 1981
Earlier work this paper cites.
Is imitation learning the route to humanoid robots?
Schaal, S · 1999
Earlier work this paper cites.
Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., and Singh, S · 1999
Earlier work this paper cites.
Variational learning for switching state-space models
Ghahramani, Z. and Hinton, G. E · 2000
Earlier work this paper cites.
A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., and Browning, B · 2009
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Learning stochastic recurrent networks
Bayer, J. and Osendorfer, C · 2014
Earlier work this paper cites.
Variational particle approximations
Kulkarni, T. D., Saeedi, A., and Gershman, S · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
Earlier work this paper cites.
Reinforcement and imitation learning via interactive no-regret learning
Ross, S. and Bagnell, J. A · 2014
Earlier work this paper cites.
Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
Cited alongside, same era.
A recurrent latent variable model for sequential data
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A. C., and Bengio, Y · 2015
Cited alongside, same era.
Inferring algorithmic patterns with stack-augmented recurrent nets
Joulin, A. and Mikolov, T · 2015
Cited alongside, same era.
Neural programmer-interpreters
Reed, S. and De Freitas, N · 2015
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
Schulman, J., Heess, N., Weber, T., and Abbeel, P · 2015
Cited alongside, same era.
Sequential neural models with stochastic layers
Deeply aggrevated: Differentiable imitation learning for sequential prediction
Sun, W., Venkatraman, A., Gordon, G. J., Boots, B., and Bagnell, J. A · 2017
Later among the works it cites.
Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Zhang, R., Isola, P., and Efros, A. A · 2017
Later among the works it cites.
Variational option discovery algorithms
Achiam, J., Edwards, H., Amodei, D., and Abbeel, P · 2018
Later among the works it cites.
Leveraging grammar and reinforcement learning for neural program synthesis
Bunel, R., Hausknecht, M., Devlin, J., Singh, R., and Kohli, P · 2018
Later among the works it cites.
Virel: A variational inference framework for reinforcement learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fraccaro, M., Sønderby, S. K., Paquet, U., and Winther, O · 2016
Cited alongside, same era.
Generative adversarial imitation learning
Ho, J. and Ermon, S · 2016
Cited alongside, same era.
Neural program lattices
Li, C., Tarlow, D., Gaunt, A. L., Brockschmidt, M., and Kushman, N · 2016
Cited alongside, same era.
Robustfill: Neural program learning under noisy i/o
Devlin, J., Uesato, J., Bhupatiraju, S., Singh, R., Mohamed, A.-r., and Kohli, P · 2017
Cited alongside, same era.
Backpropagation through the void: Optimizing control variates for black-box gradient estimation
Grathwohl, W., Choi, D., Wu, Y., Roeder, G., and Duvenaud, D · 2017
Cited alongside, same era.
Imitation learning: A survey of learning methods
Hussein, A., Gaber, M. M., Elyan, E., and Jayne, C · 2017
Cited alongside, same era.
Fellows, M., Mahajan, A., Rudner, T. G., and Whiteson, S · 2018
Later among the works it cites.
Parametrized hierarchical procedures for neural programming
Fox, R., Shin, R., Krishnan, S., Goldberg, K., Song, D., and Stoica, I · 2018
Later among the works it cites.
Reinforcement learning and control as probabilistic inference: Tutorial and review
Levine, S · 2018
Later among the works it cites.
Improving neural program synthesis with inferred execution traces
Shin, R., Polosukhin, I., and Song, D · 2018
Later among the works it cites.
Execution-guided neural program synthesis
Chen, X., Liu, C., and Song, D · 2019
Closest in time.
Multi-task hierarchical imitation learning for home automation
Fox, R., Berenstein, R., Stoica, I., and Goldberg, K · 2019
Closest in time.