An Adversarial Objective for Scalable Exploration

——, “A possibility for implementing curiosity and boredom in model-building neural controllers,” in Proc. of the First Int. Conf. on Simul. of Adaptive Behavior , 1990

1990

Earlier work this paper cites.

J. Schmidhuber, “Making the world differentiable: On using self-supervised fully recurrent neural networks for dynamic reinforcement learning and planning in non-stationary environments,” Tech. Rep., 1990

1990

Earlier work this paper cites.

R. Bajcsy and M. Campos, “Active and exploratory perception,” CVGIP: Image Understanding , vol. 56, no. 1, pp. 31–40, 1992

1992

Earlier work this paper cites.

H. S. Seung, M. Opper, and H. Sompolinsky, “Query by committee,” COLT , 1992

1992

Earlier work this paper cites.

D. D. Lewis and W. A. Gale, “A sequential algorithm for training text classifiers,” SIGIR , 1994

1994

Earlier work this paper cites.

D. Cohn, L. Atlas, and R. Ladner, “Improving Generalization with Active Learning,” Machine Learning , vol. 15, no. 2, pp. 201–221, 1994

1994

Earlier work this paper cites.

D. A. Cohn, Z. Ghahramani, and M. I. Jordan, “Active learning with statistical models,” NIPS , 1996

1996

Earlier work this paper cites.

R. Rubinstein, “The Cross-Entropy Method for Combinatorial and Continuous Optimization,” Methodology And Computing In Applied Probability , vol. 1, no. 2, pp. 127–190, 1999

1999

Earlier work this paper cites.

N. Roy and A. McCallum, “Toward optimal active learning through sampling estimation of error reduction,” ICML , 2001

2001

Earlier work this paper cites.

M. Kearns and S. Singh, “Near-optimal reinforcement learning in polynomial time,” Machine Learning , vol. 49, no. 2-3, pp. 209–232, 2002

2002

Earlier work this paper cites.

P. Melville and R. J. Mooney, “Diverse ensembles for active learning,” ICML , 2004

2004

Earlier work this paper cites.

R. Moskovitch, N. Nissim, D. Stopel, C. Feher, R. Englert, and Y. Elovici, “Improving the detection of unknown computer worms activity using active learning,” AAAI , 2007

2007

Earlier work this paper cites.

B. Settles, M. Craven, and S. Ray, “Multiple-instance active learning,” NIPS , 2008

2008

Earlier work this paper cites.

B. Settles, “Active learning literature survey,” University of Wisconsin–Madison, Computer Sciences Technical Report 1648, 2009

2009

Earlier work this paper cites.

A. J. Joshi, F. Porikli, and N. Papanikolopoulos, “Multi-class active learning for image classification,” CVPR , 2009

2009

Cited alongside, same era.

J. Schmidhuber, “Formal theory of creativity, fun, and intrinsic motivation (1990;2013;2010),” IEEE Trans. on Auton. Ment. Dev. , vol. 2, no. 3, pp. 230–247, 2010

2010

Cited alongside, same era.

R. Sznitman and B. Jedynak, “Active testing for face detection and localization,” IEEE Trans. on Pattern Analysis and Machine Intelligence , vol. 32, no. 10, pp. 1914–1920, 2010

2010

Cited alongside, same era.

N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel, “Bayesian active learning for classification and preference learning,” arXiv preprint arXiv:1112.5745 , 2011

Original

2011

Cited alongside, same era.

M. Lopes, T. Lang, M. Toussaint, and P.-Y. Oudeyer, “Exploration in model-based reinforcement learning by empirically estimating learning progress,” NIPS , 2012

C. Finn and S. Levine, “Deep Visual Foresight for Planning Robot Motion,” ICRA , 2017

Original

2017

Later among the works it cites.

Y. Burda, H. Edwards, A. Storkey, and O. Klimov, “Exploration by random network distillation,” arXiv preprint arXiv:1810.12894 , 2018

Original

2018

Later among the works it cites.

T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” arXiv preprint arXiv:1801.01290 , 2018

Original

2018

Later among the works it cites.

A. X. Lee, R. Zhang, F. Ebert, P. Abbeel, C. Finn, and S. Levine, “Stochastic Adversarial Video Prediction,” arXiv preprint , apr 2018

2018

Later among the works it cites.

A. Jaegle, V. Mehrpour, and N. Rust, “Visual novelty, curiosity, and intrinsic reward in machine learning and the brain,” arXiv preprint arXiv:1901.02478 , 2019