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To solve a text-based game, an agent needs to formulate valid text commands for a given context and find the ones that lead to success.
Bidirectional recurrent neural networks
Schuster, M. and Paliwal, K. K. (1997) · 1997
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Machine learning for spoken dialogue systems
Lemon, O. and Pietquin, O. (2007) · 2007
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Sample-efficient batch reinforcement learning for dialogue management optimization
Pietquin, O., Geist, M., Chandramohan, S., and Frezza-Buet, H. (2011) · 2011
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Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y. (2014) · 2014
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On the properties of neural machine translation: Encoder-decoder approaches
Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014) · 2014
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Glove: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C. D. (2014) · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V. (2014) · 2014
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Deep Reinforcement Learning with a Natural Language Action Space
He, J., Chen, J., He, X., Gao, J., Li, L., Deng, L., and Ostendorf, M. (2015) · 2015
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Language Understanding for Text-based Games Using Deep Reinforcement Learning
Narasimhan, K., Kulkarni, T., and Barzilay, R. (2015) · 2015
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A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Sordoni, A., Bengio, Y., Vahabi, H., Lioma, C., Simonsen, J. G., and Nie, J.-Y. (2015) · 2015
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M. (2015) · 2015
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Pointer Networks
Vinyals, O., Fortunato, M., and Jaitly, N. (2015) · 2015
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Sample-efficient deep reinforcement learning for dialog control
Asadi, K. and Williams, J. D. (2016) · 2016
Pointing the Unknown Words
Gulcehre, C., Ahn, S., Nallapati, R., Zhou, B., and Bengio, Y. (2016) · 2016
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Natural Language Generation as Planning under Uncertainty Using Reinforcement Learning
Rieser, V. and Lemon, O. (2016) · 2016
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A network-based end-to-end trainable task-oriented dialogue system
Wen, T., Gasic, M., Mrksic, N., Rojas-Barahona, L. M., Su, P., Ultes, S., Vandyke, D., and Young, S. J. (2016) · 2016
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Adversarial Learning for Neural Dialogue Generation
Li, J., Monroe, W., Shi, T., Jean, S., Ritter, A., and Jurafsky, D. (2017) · 2017
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A Joint Model for Question Answering and Question Generation
Wang, T., Yuan, X., and Trischler, A. (2017) · 2017
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Learning end-to-end goal-oriented dialog
Bordes, A. and Weston, J. (2016) · 2016
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Policy networks with two-stage training for dialogue systems
Fatemi, M., El Asri, L., Schulz, H., He, J., and Suleman, K. (2016) · 2016
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Counting to Explore and Generalize in Text-based Games
Yuan, X., Côté, M.-A., Sordoni, A., Laroche, R., Tachet des Combes, R., Hausknecht, M., and Trischler, A. (2018a)
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Generating Diverse Numbers of Diverse Keyphrases
Yuan, X., Wang, T., Meng, R., Thaker, K., He, D., and Trischler, A. (2018b)
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Côté, M.-A., Kádár, A., Yuan, X., Kybartas, B., Barnes, T., Fine, E., Moore, J., Hausknecht, M., Asri, L. E., Adada, M., Tay, W., and Trischler, A. (2018) · 2018
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Learning how not to act in text-based games
Haroush, M., Zahavy, T., Mankowitz, D. J., and Mannor, S. (2018) · 2018
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Using reinforcement learning to learn how to play text-based games
Zelinka, M. (2018) · 2018
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