Automatic state abstraction from demonstration

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1243-1248

  Cobo, Luis C ^a   Zang, Peng ^b   Isbell Jr , Charles L ^b   Thomaz, Andrea L ^b  

^a Georgia Institute of Technology   (United States)

^b Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX TASK; LEARNING FROM DEMONSTRATION; REINFORCEMENT LEARNING METHOD; STATE ABSTRACTION; TRAINING EXAMPLE;

ABSTRACTING; ARTIFICIAL INTELLIGENCE; REINFORCEMENT LEARNING;

DEMONSTRATIONS;

EID: 84881076324     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-211     Document Type: Conference Paper

References (17)

1. Pieter Abbeel and Andrew Y. Ng. Apprenticeship learning via inverse reinforcement learning. In International Conference on Machine Learning, page 1. ACM, 2004.
2. R. Aler, O. Garcia, and JM Valls. Correcting and improving imitation models of humans for robosoccer agents. In IEEE Congress on Evolutionary Computation, volume 3, 2005.
3. Brenna D. Argall, Sonia Chernova, Manuela Veloso, and Brett Browning. A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5):469-483, May 2009.
4. C. Elkan and K. Noto. Learning classifiers from only positive and unlabeled data. In International Conference on Knowledge Discovery and Data Mining, pages 213-220. ACM, 2008.
5. A.M. Farahmand, M. Ghavamzadeh, C. Szepesvari, and S. Mannor. Regularized policy iteration. Advances in Neural Information Processing Systems, 21:441-448, 2009.
6. M.A. Hall. Correlation-based feature selection for machine learning. PhD thesis, 1999.
7. Nicholas K Jong and Peter Stone. State Abstraction Discovery from Irrelevant State Variables. International Joint Conference on Artificial Intelligence, pages 752-757, 2005.
8. P.W. Keller, S. Mannor, and D. Precup. Automatic basis function construction for approximate dynamic programming and reinforcement learning. In International Conference on Machine learning, pages 449-456. ACM, 2006.
9. J.Z. Kolter and A.Y. Ng. Regularization and feature selection in least-squares temporal difference learning. In International Conference on Machine Learning, pages 521-528. ACM, 2009.
10. F. Letouzey, F. Denis, and R. Gilleron. Learning from positive and unlabeled examples. In Algorithmic Learning Theory, pages 71-85. Springer, 2009.
11. S. Ray N. Mehta, M. Wynkoop, P. Tadepalli, and T. Dietterich. Automatic induction of maxq hierarchies. In Proceedings of the Hierarchical Organization of Behavior Workshop. 21st Conference on Neural Information Processing Systems, 2007.
12. A.Y. Ng, H.J. Kim, M.I. Jordan, S. Sastry, and S. Ballianda. Autonomous helicopter flight via reinforcement learning. Advances in Neural Information Processing Systems, 16, 2004.
13. R. Parr, C. Painter-Wakefield, L. Li, and M. Littman. Analyzing feature generation for value-function approximation. In International Conference on Machine learning, pages 737-744. ACM, 2007.
14. S.P. Singh, Tommi Jaakkola, and M.I. Jordan. Learning without state-estimation in partially observable Markovian decision processes. In International Conference on Machine Learning, pages 284-292, 1994.
15. W.D. Smart and L.P. Kaelbling. Effective reinforcement learning for mobile robots. In IEEE International Conference on Robotics and Automation, volume 4, pages 3404-3410, 2002.
16. P. Zang, P. Zhou, D. Minnen, and C.L. Isbell. Discovering options from example trajectories. In Proceedings of the 26th Annual International Conference on Machine Learning. ACM New York, NY, USA, 2009.
17. M.L. Zhang and Z.H. Zhou. MLKNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038-2048, 2007.