Learning Predictive Representations from a history

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 969-976

  Wiewiora, Eric ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MARKOV MODELS; PREDICTIVE REPRESENTATIONS; PREDICTIVE STATE REPRESENTATIONS (PSR);

ALGORITHMS; INFORMATION THEORY; MATHEMATICAL MODELS; OPTIMAL SYSTEMS;

LEARNING SYSTEMS;

EID: 31844439543     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

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3. Jaeger, H. (2000a). Discrete-time, discrete-valued observable operator models: a tutorial (Technical Report). GMD - German National Research Center for Information Technology.
4. Jaeger, H. (2000b). Observable operator models for discrete stochastic time series. Neural Computation, 12.
5. James, M. R., & Singh, S. (2004). Learning and discovery of predictive state representations in dynamical systems with reset. Twenty-first International Conference on Machine Learning.
6. Kretzschmar, K. (2003). Learning symbol sequences with observable operator models (Technical Report). GMD - German National Research Center for Information Technology.
7. Littman, M., Sutton, R., & Singh, S. (2001). Predictive representations of state. Advances in Neural Information Processing Systems.
8. Rosencrantz, M., Gordon, G., & Thrun, S. (2003). Learning low dimensional predictive representations. Twenty-first International Conference on Machine Learning.
9. Singh, S., James, M., & Rudary, M. (2004). Predictive state representations: A new theory for modelling dynamical systems. Conference on Uncertainty in Artificial Intelligence.
10. Singh, S., Littman, M., Jong, N., Pardoe, D., & Stone, P. (2003). Learning predictive state representations. Twentieth International Conference on Machine Learning.
11. Sutton, R., Precup, D., & Singh, S. (1999). Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112.