Learning predictive state representations using non-blind policies

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 129-136

  Bowling, Michael ^a   McCracken, Peter ^a   James, Michael ^b   Neufeld, James ^a   Wilkinson, Dana ^c  

^a UNIVERSITY OF ALBERTA   (Canada)

^b TOYOTA TECHNICAL CENTER   (United States)

^c UNIVERSITY OF WATERLOO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; DECISION THEORY; LEARNING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; OPTIMIZATION;

NON BLIND POLICIES; NON MARKOVIAN DECISION PROCESSES; PREDICTIVE STATE REPRESENTATIONS; PREDICTIVE STATE REPRESENTATIONS (PSR);

PREDICTIVE CONTROL SYSTEMS;

EID: 33749258231     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Cassandra, A. (1999). Tony's POMDP file repository page. http://www.cs.brown.edu/research/ai/pomdp/examples/.
2. James, M. R., & Singh, S. (2004). Learning and discovery of predictive state representations in dynamical systems with reset. Proceedings of the 21st International Conference on Machine Learning (ICML) (pp. 719-726).
3. Koller, D., Megiddo, N., & von Stengel, B. (1996). Efficient computation of equilibria for extensive two-person games. Games and Economic Behavior, 14, 247-259.
4. Littman, M., Sutton, R. S., & Singh, S. (2002). Predictive representations of state. Advances in Neural Information Processing Systems 14 (NIPS) (pp. 1555-1561). MIT Press.
5. McCracken, P., & Bowling, M. (2006). Online learning of predictive state representations. Advances in Neural Information Processing Systems 18 (NIPS). MIT Press. To appear.
6. Rosencrantz, M., Gordon, G., & Thrun, S. (2004). Learning low dimensional predictive representations. Proceedings of the 21st International Conference on Machine Learning (ICML) (pp. 695-702).
7. Singh, S., James, M. R., & Rudary, M. R. (2004). Predictive state representations: A new theory for modeling dynamical systems. Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (UAI) (pp. 512-519).
8. Singh, S., Littman, M., Jong, N., Pardoe, D., & Stone, P. (2003). Learning predictive state representations. Proceedings of the Twentieth International Conference on Machine Learning (ICML) (pp. 712-719).
9. Wiewiora, E. (2005). Learning predictive representations from a history. Proceedings of the 22nd International Conference on Machine Learning (ICML) (pp. 969-976).
10. Wolfe, B., James, M. R., & Singh, S. (2005). Learning predictive state representations in dynamical systems without reset. Proceedings of the 22nd International Conference on Machine Learning (ICML) (pp. 985-992).