Deep reinforcement learning with a natural language action space

54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers

Volumn 3, Issue , 2016, Pages 1621-1630

  He, Ji ^a   Chen, Jianshu ^b   He, Xiaodong ^b   Gao, Jianfeng ^b   Li, Lihong ^b   Dengt, Li ^b   Ostendor, Mari ^a  

^a University of Washington   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; DEEP NEURAL NETWORKS; INTERFACES (COMPUTER); MACHINE LEARNING; NETWORK ARCHITECTURE; VECTOR SPACES;

ACTION DESCRIPTIONS; ACTION SPACES; INTERACTION FUNCTIONS; NATURAL LANGUAGES; NOVEL ARCHITECTURE; Q-FUNCTIONS; Q-LEARNING; RELEVANCE NETWORKS;

REINFORCEMENT LEARNING;

EID: 85011842203     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/p16-1153     Document Type: Conference Paper

References (25)

1. E. Adams. 2014. Fundamentals of game design. Pearson Education.
2. S. R. K. Branavan, H. Chen, L. Zettlemoyer, and R. Barzilay. 2009. Reinforcement learning for mapping instructions to actions. In Proc. of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th IJCNLP, pages 82-90, August.
3. S. R. K. Branavan, D. Silver, and R. Barzilay. 2011. Learning to win by reading manuals in amonte-carlo framework. In Proc. of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 268-277. Association for Computational Linguistics.
4. R. Collobert and J. Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proc. of the 25th International Conference on Machine learning, pages 160-167. ACM.
5. G. E. Dahl, D. Yu, L. Deng, and A. Acero. 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Audio, Speech, and Language Processing, IEEE Transactions on, 20(1):30-42.
6. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag., 29(6):82-97.
7. P-S. Huang, X. He, J. Gao, L. Deng, A. Acero, and L. Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proc. of the ACM International Conference on Information & Knowledge Management, pages 2333-2338. ACM.
8. R. Kiros, Y. Zhu, R. R. Salakhutdinov, R. Zemel, R. Urtasun, A. Torralba, and S. Fidler. 2015. Skipthought vectors. In Advances in Neural Information Processing Systems, pages 3276-3284.
9. A. Krizhevsky, I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105.
10. Q. V. Le and T. Mikolov. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning.
11. Y. LeCun, Y. Bengio, and G. Hinton. 2015. Deep learning. Nature, 521(7553):436-444.
12. L. Li, J. D. Williams, and S. Balakrishnan. 2009. Reinforcement learning for spoken dialog management using least-squares policy iteration and fast feature selection. In Proceedings of the Tenth Annual Conference of the International Speech Communication Association (INTERSPEECH-09), page 24752478.
13. T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra. 2016. Continuous control with deep reinforcement learning. In International Conference on Learning Representations.
14. L-J. Lin. 1993. Reinforcement learning for robots using neural networks. Technical report, DTIC Document.
15. M-T. Luong, H. Pham, and C. D. Manning. 2015. Effective approaches to attention-based neural machine translation. In Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1412-1421, September.
16. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. 2013. Playing Atari with Deep Reinforcement Learning. NIPS Deep Learning Workshop, December.
17. 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. Humanlevel control through deep reinforcement learning. Nature, 518(7540):529-533.
18. K. Narasimhan, T. Kulkarni, and R. Barzilay. 2015. Language understanding for text-based games using deep reinforcement learning. In Proc. of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1-11, September.
19. R. Nogueira and K. Cho. 2016. Webnav: A new largescale task for natural language based sequential decision making. arXiv preprint arXiv:1602.02261.
20. K. Schettler and S. Young. 2002. Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning. In Proc. of the second International Conference on Human Language Technology Research, pages 12-19.
21. I. Sutskever, O. Vinyals, and Q. V. Le. 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pages 3104-3112.
22. R. S. Sutton and A. G. Barto. 1998. Reinforcement learning: An introduction, volume 1. MIT press Cambridge.
23. G. Tesauro. 1995. Temporal difference learning and td-gammon. Communications of the ACM, 38(3):58-68.
24. C. JCH Watkins and P. Dayan. 1992. Q-learning. Machine learning, 8(3-4):279-292.
25. S. Young, M. Gasic, B. Thomson, and J. D. Williams. 2013. Pomdp-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 101(5):1160-1179.